COLD: Causal reasOning in cLosed Daily activities
Authors: Abhinav Joshi
Areeb Ahmad
Ashutosh Modi
Venue: NeurIPS 2024 poster (2024)
Keywords: Causal Common Sense
Causal NLP
LLMs
Commonsense Reasoning
Primary area: natural_language_processing
TL;DR: We present a new perspective on causal reasoning in NLP by using a closed system (defined by real-world commonsense activities) and propose a framework with underlying causal graphs. The causal queries help validate causal reasoning in LLMs.

Abstract: Large Language Models (LLMs) have shown state-of-the-art performance in a variety of tasks, including arithmetic and reasoning; however, to gauge the intellectual capabilities of LLMs, causal reasoning has become a reliable proxy for validating a general understanding of the mechanics and intricacies of the world similar to humans. Previous works in natural language processing (NLP) have either focused on open-ended causal reasoning via causal commonsense reasoning (CCR) or framed a symbolic representation-based question answering for theoretically backed-up analysis via a causal inference engine. The former adds an advantage of real-world grounding but lacks theoretically backed-up analysis/validation, whereas the latter is far from real-world grounding. In this work, we bridge this gap by proposing the COLD (Causal reasOning in cLosed Daily activities) framework, which is built upon human understanding of daily real-world activities to reason about the causal nature of events. We show that the proposed framework facilitates the creation of enormous causal queries (∼ 9 million) and comes close to the mini-turing test, simulating causal reasoning to evaluate the understanding of a daily real-world task. We evaluate multiple LLMs on the created causal queries and find that causal reasoning is challenging even for activities trivial to humans. We further explore (the causal reasoning abilities of LLMs) using the backdoor criterion to determine the causal strength between events.

COLD: Causal reasOning in cLosed Daily activities
Abhinav Joshi'
Ashutosh Modi Department of Computer Science and Engineering Indian Institute of Technology Kanpur (IIT Kanpur)
Areeb Ahmad"
Kanpur, India
{ajoshi, areeb, asce aitk.ac.ii in
Abstract
Large Language Models (LLMs) have shown state-of-the-ar performance in a vari- ety of tasks, including arithmetic and reasoning; however, to gauge the intellectual capabilities of LLMs, causal reasoning has become reliable proxy for validating a general understanding of the mechanics and intricacies the world similar to hu- mans. Previous works in natural language processing (NLP) have either focused on open-ended causal reasoming via causal commonsense reasoning (CCR) or framed symbolic representation- baseed question answering for theoretically backed-up world
analysis via a causal inference engine. The former adds an advantage grounding but lacks theoretically backed-up analysis/validation whereas the latter is far from real-world grounding. In this work, we bridge this gap by proposing the COLD (Causal reasOning in eLased Daily activities) framework, which is built upon human understanding of daily real-world activities to reason about the causal nature of events. We show that the proposed framework facilitates the creation of enormous causal queries (~ 9 million) and comes close to the mini-turing test, simulating causal reasoning to evaluate the understanding of a daily real-world task. We evaluate multiple LLMs on the created causal queries and find that causal reasoning challenging even for activities trivial humans. We further explore (the causal reasoning abilities of LLMs) using the backdoor criterion to determine the causal strength between events.
1 Introduction
In recent times, Large Language Models (LLMs) have shown remarkable generalization capabilities [Devlin et al., 2019, Radford et al., 2019, Brown et al., 2020]. Consequently, the ability to perform causal reasoning (often considered a core feature of intelligence [Penn and Povinelli, 2007, Pearl and Mackenzie, 2018)) has sparked research interest in the context of LLMs, aiming to answer if causal reasoning is possible with LLMS [Weber et al., 2020, Jin et al., 2023, 2024, Cohrs et al., 2023, Romanou et al., 2023, Yang et al., 2023, Mitchell et al., 2023, Vashishtha et al., 2023, Stolfo et al., 2023]. On a broader level, there are two lines of work; first, that treats the causal reasoning via learning relationships between the events that are grounded in the real world [Gordon al., 2012, Ho et al., 2022, Zečević et al., 2023, Zhang et al., 2023, Wang et al., 2023]. Second line of work relies on causal inference engine and establishes relationships between variables via symbolic representation [Jin et al., 2023, 2024]. The former relies on understanding real-world events but lacks formal definitions that adhere the causal inference theory. The latter solves the issue using a causal inference engine but uses symbolic representations not grounded in the world, making the causal queries more like a test for the understanding of causal theory. Though the first line of work includes real-world events, the causal queries are often limited and could be answered by memorizing the causal relationships between the events. Recent findings that include rigorous analysis using a causal
'Equal Contribution
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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inference engine claim LLMs to be "Causal Parrots" [Zečević et al., 2023], i.e. the LLMs tend to pick up (memorize) patterns in the training data to perform well on the causal reasoning benchmarks. Moreover, some initial findings by Tang et al. [2023] suggest that LLMs perform significantly better when semantics are consistent with commonsense but struggle to solve symbolic tasks, pointing towards semantic representation to be better for proper validation of L.MMs, leading to a conclusion that an in-depth analysis using real-world events is necessary.
In this work, we bridge the gap between the two approaches by proposing the COLD (Causal
reasOning in cLosed Daily activities) framework, based on the human understanding of real- world daily activities capturing commonsense (for example, "making coffee," " "boarding an airplane," " etc.), that adheres to causal theory literature. It is more natural to frame real-life reasoning-base based queries via language; consequently, we follow the literature on Causal Commonsense Reasoning (CCR), which studies the relationships between real- world events (described via natural language). CCR is a non-trivial task of estimating the cause- and-effect relationship between events that are studied under the umbrella of commonsense reasoning [Kuipers, 1984, Gordon et al., 2012, Zhang et al., 2022b, Wang et al., 2023, Chun et al., 2023, Du et al., 2022]. The events in CCR generally refer to actions taking place in ac- tivity in the real world. For example, consider the activity of "traveling by an airplane" given in Fig. 1, where the occurrence of all the events is confounded by universal variable U ("intention to perform a task"). Moreover, there are a few events that cause one another. For ex- ample, the event cchcckingg in luggage" (E1) caused the occurrence of events like "waitim ting at the luggage belt" (E2) after the flight, i.e., in an alternate universe where one does not checks in luggage and goes with the cabin bags, will never wait for their luggage after the flight has landed. Moreover, some of the events have no causal impact, like "find the boarding gate" (Fi) has no causal relationship with "checking luggage" (F1). More formally,
to perforns Browsling plinting tree, gooers shopping.ent etc
Bh ue
Temaorra Link
Take
Causil Link
Cosso Evest
Efest Evertdi
Figure 1: U denotes the unobserved variables, con- founding all events present in real- world activ- ity. In an activity, some events cause other events to happen. For example, in "traveling by an air- plane", ", the event of "check-in luggage" causes events like "taking back luggage." "
( = P(E E1)) EE (d))) (EE = Po (E1) - IP EE (d)(((E))))
(I)
where do(-) denotes the do operator [Pearl, 2012] showing the intervention on E,, and A is the causal estimand capturing the causal strength between two events, i.e., A(E + E2) is expected to be higher when compared A(E, + Ez). Note, CCR excludes the causal events that are beyond the reach of commonsense knowledge. for example, does "planting trees" have direct impact on the "rainy season"? or does "providing free education" improve the "economic conditio tion of the country/state", does "carpooling ing" directly impact "air pollution" etc. noteworthy point concerning causality is that though the logical temporal (or prototypical) order of these events provides a weak signal about causal relationships, temporal precedence does not always imply causation ($2). For example, one could erroneously argue that "boarding a plane" is also the cause of "waiting at the luggage belt" since without "boarding plane," " one cannot wait for the luggage belt.
For building a causal reasoning framework (based on CCR) around real-life daily activities, one would require a few primary features readily available: 1) Clear distinction between the events, i.c., the events should encapsulate describing a particular step in an activity, 2) Causal Dependency between the variables/events, i.e., there should be some events causing other events to occur. 3) Causal independence of events with the rest of the world, i.e., the occurrence of events should be independent of events that are not part of the activity (i.e., the covariates are balanced out). We found that "Scripts" [Schank, 1975, Schank and Abelson, 1975] provide a concrete medium that satisfies all these requirements. Scripts are defined as a sequence of events describing prototypical activity, such as going to a restaurant, and hence capture commonsense knowledge about the world. [Schank and Abelson, 1975, Modi et al., 2016, Wanzare et al., 2016, Ostermann et al., 2018, Modi, 2016, 2017, Modi et al., 2017, Modi and Titov, 2014]. Moreover, different people have similar understandings of
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Pre activity
Causal Graph for the Activity "Going Grocery Shopping"
world
The pre-worid and post-world activities involve various place outside the context acthities, making them Independent of the events
int
happening during the activity.
Eurr-on car
by walk
go bus
make list
drive to grocery
Smps performed in daily actirties
(cap (capturring co cammo nosmonseensa knowledge)
carry
Grocery Shopp ing
Fhying in en airaiiaan airplane
groceries
Taking a Bath
Riding on Bus
get list from car
store
venicle from parking
Going
to checkout
present coupons
check list
put conveyor
anything left)
gett wallet
cashier scan/weigt
take bags
shop
Post actiity
USe bags
put bags in cart
get receipt
wo
Begin again
Start another activity
Start work
Colliders denoting Conditional Dependence
relax
Independent (may may not be present an activity and is not cause or offect of any of the other events)
Figure 2: Left: the figure represents the closed nature of daily real-world activities (capturing commonsense, commonly understood by humans), start and end given the context of the task, i.e., the pre-activity world and post-activity world activities marginalize out the dependence of event occurring during the activity with the rest the world. Right: Causal Graph for "going grocery shopping. Notice the collider (red nodes) makes the independent set of nodes (highlighted in different colors) unconditionally independent in the causal graph. In contrast, when given a condition on a collider ("put bags in cart", the two clusters (yellow and blue) become dependent (if collider observed, both yellow and blue clusters may have been observed as well).
the activity in the form of scripts that inherently balance out the covariates present in the real world, i.e, all the activities have the same starting and ending point and account for common exogenous and endogenous variables, providing suitable platform to establish a cause-and-effect relationship between the events. In other words, for activity like "flying in airplane, " "going grocery shopping" (also see Fig. 2, left) the events that happened before starting the activity and after completing the activity are marginalized out using common understanding of these activities by different humans and hence will have no causal relations with any of the exogenous events during the activity. Creating a causal graph for script knowledge, i.e., establishing relationships between events taking place during the activity, provides a perfect platform for creating causal queries, thus providing medium to establish CCR between events. In nutshell, make the following contributions:
We propose COLD (Causal reasOning in cl.osed Daily activities), CCR framework based on Script knowledge (daily activities involving commonsense) that provides closed system to test the understanding of causal inference grounded in the real-world. The proposed framework adheres to SUTVA (Stable Unit Treatment Value Assumption) [Cox, 1958, Rubin, 1980] by design ($3). COLD consists of activity-speciffc observational graphs (created via crowd-sourcing) and causal graphs. Further, COLD facilitates creating enormous number of causal queries (e.g., 2, 887, 950 per activity) via causal query triplets from the causal graph. This comes close to the mini-Turine Turing test [Pearl and Mackenzie, 2018], where the story becomes the understanding of the daily activity, and the sampled enormous causal queries help in the exhaustive and rigorous evaluation LMs.
We devise various design mechanisms for estimating causal strength analytically and show how the representations learned by language models can be validated.
Via detailed experimentation on the widely used open-weight language models, including encoder- only models (ROBERTa-MNLD) and autoregressive models (gpt-neo- 125M, gpt-neo-1.3B, gemma- 2b, gpt-neo-2.7B, phi-2, gpt-j-6B, Llama-2-7bcchat-hf, Mistral-7B-v0.1. gemma-7b, and Meta- Llama-3-8B) we estimate the causal reasoning capability of the learned representations. We release the framework, model code, and results via http:://ithhbbcoom//poorttttonnn Lab/CLL..
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Human Wiltten
Observational Graph (S.)
Algurithm-1 1 for creating "Cousal Query Triplets"
Caussa Query Triplets"
Workers
Queetiin Wrich calcvingg afrecc
Chelee il Toome home
SuGa ticnoad yo
offinia Owerten Wisd Phrticenngh
"Raking
Caker
Graph (ge)
Chasee 2-
pvours wu
Real-World
Commonsente Knowledge
Validating Causal Ressoning
Activitiss
D
GPTJ
Liama
Cemmaa
Nattral
Figure 3: The proposed COLD framework for evaluating LLMs for causal reasoning. The human- written Event Sequence Descriptions (ESDs) are obtained from crowdsource workers and include telegrammic-stri sequence of events when performing an activity. The Observational Graph and the Causal Graph for an activity used create causal query triplets (details in Algorithm 1), shown towards the right. Using counterfactual reasoning, "going to the kitchen" is possible without going to the market (if the ingredients
already available), making "come home with the ingredients." more plausible effect among the given choices. Similarly, in the second example, the event "going to market" has direct relation with the event "heating the oven".
2 Background
The Mini Turing Test proposed by Pearl and Mackenzie [2018] is designed in a question-answering format to validate the understanding of causal knowledge about simple story. The primary feature of mini-Turing test is the enormous number of causal queries that be framed using the underlying causal graph, which governs the occurrence of events in the story. Due to the enormoos number of causal queries, passing the mini- mini-Turing via memorization becomes combinatorially heavy, and hence, the authors argue that it can only be beaten if one has access to the underlying causal graph governing the occurrence of events (i.e., one has the ability to reason eausally about events). this work, though, we only consider more straightforward case of phoice-based causal triplets; we realize the number of causal queries that can be created is enormous and helps validate the causal reasoning abilities coming close to the mini-Turing test.
d-separation:
Establishing the independence of variables becomes non-trivial when dealing with complex interactions among multiple variables. d-separation [Pearl, 1988] facilitates the determina- tion of conditional independence between two sets of nodes X and Y a graphical model given another set of nodes Z. d-separation asserts that X and Y, given the set Z. are d-separated if all paths for every node in X and every node Y are blocked by conditioning on Z. denoted X X.I Z. A path is blocked by set of nodes Z [Pearl et al., 2016], if and only if: 1) p contains a chain of - C such that the middle node B collider A + B < C such that the collision node B or its descendant is not in Z.
nodes A -> B + or a fork A
OR 2) p contains
Backdoor Criterion: A set of variables W satisfies the backdoor criterion relative to T and Y if the following are true:
(A) W blocks all backdoor paths from T to Y i.c. blocking confounding non-causation association paths
(B) W doesn't contain any descendants of
Then, W satisfies the backdoor criterion [Pearl et al., 2016, Neal, 2020]. We make use of the backdoor criterion to estimate the causal estimand, capturing the relationship between the causal events. (Refer App. C for more detail)
3 COLD (Causal reasOning in cLosed Daily activities)
We propose COLD (Causal reasOning in cLosed Daily activities) framework for testing causal reasoning abilities natural language understanding systems such as L.LMs. Fig. gives an overview of the creation process. We use crowd-soureed data of script knowledge to create observational

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graphs which is further used along with manual intervention to create causal graphs. Subsequently, an algorithm is used create an enormous number of causal queries (causal triplets), which are further used to test LLMSs for causal reasoning. Next, we explain each of the steps in more detail.
Task Formulation: COLD is motivated by Causal Commonsense Reasoning (CCR), which define as the task of finding the strength of the cause-and-effie relationship between two events (Ei and E2) given in an activity a E AA, where A is the set of all activities. For example, for an activity like "going in an airplane", the central question is to determine the causal relationship between two events that occur during the activity (events like "checking in luggage" " and "waiting for luggage"). Since reasoning about a sequence of events is tedious (and sometimes confusing [Do et al., 1)). researchers often rely on a more plausible cause rather than defining definite causal event. For instance, COPA dataset [Gordon et al., 2012] provides premise event and a corresponding causal query question along with two choices (see Table for an example); a system is required to predict which of the two choices is most plausible cause/effect as required by the question.
Creating a Closed Causal System
Given the nature of Script knowledge (satisfying the criterion of balanced covariates, $1), we use a script corpus called DeScript [Wanzare et al., 2016] for creating the observational graphs. DeScript is a corpus with a telegram-sty- sequential description of an activity in English (e.g., baking a cake, For given activity, crowd- workers write
taking a bath, etc.). DeScript is created via a point-wise and sequential short description of various events involved in executing the activity (this one complete description is called ESD (Event Sequence Description)). DeScript collects data for a set of 40 daily activities (100 ESDs each) varying in complexity and background knowledge. Additionally, for given activity, semantically similar events from different ESDs are manually aligned by human annotators (for more details, refer to Wanzare et al. [2016]). These alignments were later used by Joshi al. [2023b,a] to create a DAG representing the overall activity. In our work, we use these DAGs as the observational distribution of an activity (g. where a E A. where A is the set of all activities). These DAGs provide a medium for generating enormous trajectories (scales from 1.6e + 16 to 1.3e + 27, also see Table 2), that are coming directly from human annotations (alignment as well as the ESDs), providing us a proxy to represent the understanding of daily activities.
Observational Distribution (Go): Note that the graphs Go. approximately represent (almost) all possible ways in which an ESD can be written for activity, providing the true observational distribution, i.e. how the combinations of events will look like while performing the activity in the real world (see App. A.3 for examples).
Causal Graphs (Go): To reason about the causal relationships between the events (nodes of Go). we would need the underlying causal graph that shows the cause of occurrence of various activities (directly or indirectly). We construct the causal graphs manually by reasoning about the independence of various events in the activity. Fig. 2 shows the pictorial representation of one of the created causal graphs for the activity "going grocery shopping. Notice that various sets of events in the graph create clusters, denoting independence between various events. For example, nodes related to make list cause the events that involve the presence of a list and do not cause events like going via car (as some of the population will not create list for shopping). Similarly, the mode of transportation (car/bus/walk) is independent of the events performed inside the store.
Causal Query Triplets: The obtained Causal Graph (G.), for an activity provides a medium to reason about causal links between the events. Notice in Fig. 2, how the red nodes (colliders) help separate out the independent event clusters. For example, the nodes 'get list from car' and 'check list (if anything is left)' being colliders, separate out the making list-related events with the rest of the graph. Similarly, the node 'put bags in cart' separates out the 'take shop cart' and 'take bags'. Another interesting property represented in the obtained causal graph is the conditional dependence between various clusters. For example, the cluster related to "get in car' is unconditionally independent of 'make list'. However, we condition on the collider ('get list from car'), they become dependent, i.c., if 'get list from car' is observed, it means that the person will have created the list as well as went by car for the grocery shopping (similarly for node "put bags in cart'). d-separation ($2) provides an easier way establish conditional/uncondiioonn independence between the set of nodes. For creating the dataset of causal queries (similar to other datasets like COPA [Gordon et al., 2012)),
need triplet of three events (premise p. Choice- 1 c1 Choice-2 62) associated with a question about "cause" or 'effect' relationship. i.e., given the premise which of the two choices is the cause/effect? (Table 1). We call these triplets Causal Query
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Table 1: The table shows examples of causal query triplets created using the causal graphs (Ge) and observational graphs (Go) in Algorithm 1. The top row is taken from the COPA dataset [Gordon et al., 2012] for the purpose of comparison. Note the examples in the table show the samples taken from the instance version.
Batzort
Preeake
Chioo-1
guedion
Cooe
lie girl frosd suz her cereal.
The turned fasost,
See porre wilk ir the Bow.
The todlet riblad uces vater.
Nato- F Flowdd free
See lest her appetite
efecl
ctxci
Tay proper sigrad desigraated
50 hree with tegree timr.
NATT for The Tieer To g off
67e11
COLD
200M03
baks sntil
canefully namor cake from
tury cff cans
00 store, bay tegredinare
first host nore
degresa.
prspare
stanaiia
COLD (3h051190
pay teta).
get recedps.
place cart Into cart corral.
67ec1
pay for ce
start at the ece-cold eide of the stone.
2o to thilf and get the reed Stare
80 beck can-ost Uhe
you cut and see La streary ahaa.
ing Limm diackeot.
Full
chediout lane.
sotch
doin
COLD (1ea1))
check trase actedases.
find
cheoss destiratier.
2o to the car.
CADc
gSC off treen.
Eo out of the acacice.
take all the lansags ous of craie.
ctec:
50 ceatination.
carry Teezzgr netenead bin shen reaches seatiration, train.
get the train platforn.
80 to ger dan center.
trarspor: low.
chosse tspe of tree.
ciesi
GOLD (1re0)
and ferstlliee
hole
for
place Lhs krez at live las of the bole,
conT dh rooca with dark.
gel tree.
cleci
pertly.
place sepling hale.
buck
(ins
tree.
cieci
signel for stas, arriiee.
stopo destinatiie.
stand and
eleci
COLD (86)
heard
whille bearsingg
orcver required fee
signal for
sitp tes.
lake evuilable seet.
Sall for the los to live
eleci
Une your stop approachee
Find bes and aLt.
Find shet Luo Lake.
ihpur stal.on.
Table 2: The table provides details of the observational graph (G.) for 5 activities. The Causal Query Triplets represent the total number of triplets generated via Algorithm 1. The instance version shows the number of samples present in the instance version (including different text instances describing the same event) of the created dataset. Table shows small sample taken for the 5 activities. Overall, the huge number of samples highlights the exhaustive nature of evaluation that can be done for LLMs.
Activity
Nodes Compact Trajectories Total Trajectories Causal Query Triplets Instance version (Num. samples)
Baking Cake
28
177030
1.3e 2
864
2887950
Riding Bus
20
13945
1.3e 17
334
834046
Going Crocery Shopping
33
626096
3.1e 26
1984
3739184
Going Train
26
133799
4.9c + 22
950
1213114
Planting Tree
23
4466
1.6e + 16
260
846046
Total Dataset Samples
4392
9,520,340
Triplets that are used to frame a causal query between the events. App. B. Algorithm presents the mechanism create a dataset of causal query triplets. We start by constructing the of possible triplets, and sort the nodes in every triplet using the topological order preset in the observational graph (a DAG). Further, using d-separation, we figure out the triplets that have one node d-separated from the other nodes. The d-separated node becomes the wrong choice. The premise and correct choice are determined from the remaining choices, leading 'cause" and 'effect' query based on the topological order, i.e., the event occurring before (temporally) in an activity becomes 'cause.' The other event becomes the plausible 'effect." Note that the temporal precedence generally assumed essential for defining causation, and it is one of the most important clues that is used to distinguish causal from other types of associations [Mill, 1898, Hill, 1965, Pearl and Verma, 1995]. For our framework, we also consider the topologically sorted order obtained from the observational graphs and use the temporal order to define the causal query triplets, i.e., the cause events will always precede the effect events (see App. B, Algorithm 1, where G. helps determine the temporal order of events). Table 1 shows a sample of the created dataset in comparison to the COPA dataset. Note that each node (premise, Choice- 1, Choice-2) in Ge also has multiple texts (instances of text describing the same event) written by different crowd-sourced workers. We can further use these text instances to enrich the created dataset by considering all the available instances to create all possible combinations. This strategy increases the scale of the created dataset by a huge margin. Overall, we found a dataset created from triplets using Algorithm I results in 4,392 tuples, which, after using the text instances, increases to 9,520,340, bringing it close to 'mini turning test" [Pearl and Mackenzie, 2018].
Adherence to SUTVA: In causal literature, a fundamentally acknowledged Stable Unit Treatment Value Assumption (SUTVA) [Cox, 1958, Rubin, 1980]) requires that for each unit (e.g., sequence of events), there is only one version of the non-treatment, i.c., for an event in the sequence, there lie only

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Table 3: The table provides evaluation results of language models over the created causal triplets.
Triplets
Model Name
cake
shopping
train
tree
bus
gpi-neo-12.5M
50.71
50.01
49.99
50.13
50.15
gpt-neo-1.3B
44.77
45.69
42.52
45.67
42.89
gemma-2b
53.76
52.19
60.57
60.71
53.64
causal triplets
gpt-neo-2.7B
50.00
50.01
50.00
50.01
50.00
phi-2
85.14
83.65
77.29
82.24
71.74
gpL-j-6B
49.59
50.02
50.29
49.92
49.93
Llamm-2--b-chattee
77.92
72.41
73.48
72.40
68.21
Mistral-7B-vO.1
77.64
69.38
68.46
72.43
69.37
gemma-7b
81.47
82.26
77.24
80.78
70.29
Meta-Llama--3-8B
80.79
76.46
76.08
78.21
67.39
two versions occurring and not occurring SUTVA plays vital role in causal inference by ensuring that each unit's treatment assignment has a consistent impact, facilitating the accurate estimation of treatment effects. Our framework closely adheres to SUTVV assumptions (details in A.
Comparison with Other Causal Datasets: We briefly compare the created dataset with the existing set of causal reasoning datasets in App. Table 5. The created dataset serves as a middle ground, having both real-world groundings as well as an underlying causal graph to create an exhaustive set of causal queries.
4 Experiments and Results
COLD provides causal query dataset for evaluating LMs for causal understanding. In particular, we consider the "Causal Query Triplets" (Table 2) coming from compact trajectories as a base and sample the instance version coming from the same skeleton. Since it is not possible evaluate all the possible causal queries that could be created using our framework, we use 10K samples for each activity to report our findings. For fair comparison between various models and better reproducibility, we freeze the sampled causal query triplets and compare the success rate over the frozen samples. We evaluate via two methods. First, as done in previous work Jin et al. [2024, 2023], Chen et al. [2024], we first experiment with various LLMs using prompt-based evaluation scheme; second, we propose other mechanisms (based on causal theory, e.g.. Average Treatment Effect) that could be used to perform an in-depth analysis of evaluating causal relationships between events.
Causal Reasoning Evaluation of LLMs via Prompts: We start with the prompt-based evaluation of recent open-weight LLMs (gpt-neo-1254) gpt-neo-1.3B, gpt-neo-2.7B 2B [Black et al., 2021], gemma- 2b [Team al., 2024]. phi-2 [Javaheripi et al., 2023], gpt-j-6B [Wang and Komatsuzaki, 2021], gemma-7b [Team et al., 2024]. Llama-2-7b-chat-h [Touvron et al., 2023]. Mistral-7B-vG. [Jiang [Dubey et al., 2024]) We frame the prompt as multi-choice
et al., 2023], and Meta-Llamas guestion-answerigg (MCQA) objective [Robinson and Wingate, 2023]. The prompt is intentionally structured so that the LLM is intended to predict single choice token (Such as " A", "B", etc.). Robinson and Wingate [2023] highlight the advantages of MCQA-based evaluation over cloze evaluation [Brown et al., 2020](wher) the LLMs are expected generate the entire answer cloze test), leading to significant boost in various tasks, including commonsense-basee tasks. App. E, Fig. presents various prompt templates for autoregressive experiments, and App. Fig. shows a few qualitative examples for the framed causal query templates. Table 3 shows the success rate obtained for various LLMs. The success rate corresponds to the percentage of queries where the LLM predicts the desired choice. We observe that reasoning causally about simple daily activities is challenging when a rigorous test is framed, validating the dependencies between the events. Overall, for the more common activities like baking cake and going grocery shopping, the LLMs perform better when compared to activities like boarding a bus or planting a tree. We also experimented with another version of the dataset, where incorrect choice may correspond to temporally plausible but causally implausible events. The results drop significantly in this case; details and results are provided in the App. F.I.
Evaluation using Average Treatment Effect (ATE) (A): Computing the Average Treatment Effect (A) helps establish the strength of causal links given a context (Eq. 1). In our setup, to estimate
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P(y]do(x)) (i.e., the causal estimand) from statistical estimands (obtained from observational dis- tribution), we make certain reasonable assumptions about the underlying process that governs the relation among variables/events and then utilize the implications of these assumptions. For any activ- ity taking place, the causal relationships between two events E1 and Eg may have causal link along with non-causal link through a set of confounders z. We define the confounder = {t, E T,, where T denotes all the trajectories (sequence of events) from the start of the activity till the event E, The temporal nature of events makes this assumption suitable since the occurrence of En and Es can be confounded by all the events preceding Ej. Note the possibility of unobserved confounders (events that are not explicitly mentioned but may be affecting the mentioned events) in our case is removed due to two reasons: 1) Keeping a closed system representation with large number of diverse scripts (written by humans) helps cover the set of most generic and diverse events either implicitly or explicitly as a part of the activity, and 2) The causal reasoning goal is restricted to figuring out the causal effect between the events that are present explicitly. Assuming the unmentioned events have insignificant effects, we can establish that there will not be any unobserved confounders. This assumption makes the observed confounders satisfy the backdoor criterion [Pearl, 1993] that make sufficient adjustment sets. By using the backdoor criterion (App. C), the interventional distributions are estimated follows:
P(Ez)do(Ea)) = EEE P. E1, = t.)
tcT
Note that the true observational distribution, i.e. p. (Fg) E1, 2) and p. (z) both are unknown and have be approximated (f( EE, =) and p(2)). Further, we describe ways can be estimated via multiple design mechanisms. Due to space limitation, we only describe the ş estimation via language models below and move the statistical analysis using the original trajectories and observational graphs to the App. D.2.
ATE using Language Models Since pre-trained LMs capture world knowledge [Devlin et al., 2019, Brown et al., 2020, Li et al., 2023, Nanda et al., 2023, Karvonen, 2024], these provide a suitable proxy for establishing relationships between these events. For our experiments, reasoning capability of language models, i.e., to reason about the temporal order of various events, i.., given an event, what is the likelihood of the occurrence of another event? We further ask if this can be used to estimate the causal relationship between the events (a similar strategy is used by Zhang et al. [2022b] for zero-shot causal estimation). It worth noting that for these activities about daily activities, one way to find causes is to establish the temporal likelihood of the events. For each of the multiple language models, we frame the temporal prediction differently.
consider simple
Encoder-only Models: For BERT-based models trained for mask token prediction, we model the temporal prediction using the probability assigned to the mask tokens "before" and "after" [Zhang et al., 2022b]. Given two events E₁ and Ez, the temporal link is predicted using a prompt like E! <mask> Ez, and the scores corresponding to the before and after tokens are collected. App. D, Fig. 7, the top row highlights the prompt template used for BERT-based models. For encoder-only experiments, we consider RoBERTa MNLI [Liu et al., 2019].
Decoder-only Models:
For other language models that are autoregressive in nature, we modify the prompt to predict the temporal order as the last token. We again use the MCQA-based prompting style to frame the temporal order query by providing "before" and "after" as the options the prompt. App. D, Fig. 7, the bottom row highlights the prompt template used for Decoder-only Models.
We utilize the SUTVA assumption in the proposed framework to devise an intervention over trajectory in natural language form. App. D, Fig. 8 shows the style of intervention made by an event (E₂) taking place (do( En )) or not taking place do(-FF).
Interventions:
Given the above strategies, LMS can be used to evaluate f = = t). by feeding the prompt that contains E,, Eg and the 2 t) and predict the temporal nature between E, and Eg, given trajectory= = L. Further, applying the backdoor criterion for multiple trajectories T, we obtain
1 Σ T
P. (M2 (o((())))) =
E1, t)
IET
(3)
Pa (E2gdd((-Ea) = T
p(Ez) -1,
8

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Table 4: Accuracy the causal triplets for various A estimates. The blue text denotes the improvements by backdoor adjustments over the temporal scheme for multiple language models. Bold text represents the best-performing method for particular activity.
I estimation
Scheme ATE
Cake
Shopping
Train
Tree
Bus
Original Trajectories
A
28.20
34.30
31.10
30.10
30.40
An
30.40
30.10
29.80
28.60
25.4
Observational Graphs
A,
40.90
47.10
40.30
37.60
40.10
RoBERTa MNLI
46.80
54.00
45.50
52.70
43.00
gpt-neo-125M
47.70
55,50
55.50
53.60
48.20
gpt-neo-1.38
47.40
45.40
53.30
43.40
52.90
gemma-2h
43.80
41.70
52.20
49.70
49.80
Temporal
gpt-neo-2.78
50.10
48,90
52.40
47.60
53.70
Language Models
phi-2
60.30
59.20
56,90
70.30
49.40
gpt-j-6B
49.50
46.40
56.00
62.70
56.00
Llama-2-7b-chat-br
38.90
42.10
$1.00
40.70
47.80
Mistral-7B-v0.11
50.90
54.40
64.50
60.50
62.30
gemma-76 7b
46.8
54.00
45,50
52.70
43.00
Meta-Llama-3-8B
58.20
54.10
55.6
55.00
64.00
AM (RoBERTa MNLI)
59.20
54.40
56,30
57.50
53.30
Backdoor Adjustments
AM (gpt-neo-1255) 125M)
59.20
55.10
50.50
52.10
45.50
AM (gpt-neo-I.3B) 1.3B)
51.30
50.70
55.00
43.90
49.00
AM (gemma-2b)
44.50
45.30
52.60
63.50
43.90
AM (gpt-neo-2.7B)
49.10
51.30
51.50
54.00
51.40
Language Models
AM (phi-2)
57.00
66.00
62.10
57.10
45.80
AM (gpt-j-6B)
51.30
45.60
50.50
49.10
46.00
A (Llama-2-7b-chat-bn)
62.60
64.60
68.50
70.50
63.80
M (Mistral-7B-vO.1)
63.90
71.40
73.70
61.30
67.00
AM (gemma-7b)
72.80
77.80
73.60
71.90
62.40
AM (Meta-Llama-3-8BB)
66.00
70.20
68.40
62.00
63.40
which can further be used to estimate the causal strength between the events E₁ and Eg.
AM (.M () (.M(Faldo(EEa)
Using the multiple A estimates defined above, estimate the causal strength between the events available for an activity. We follow the scheme presented in the App. B, Algorithm 2 to compute the performance in terms of success rates.
Temporal Scheme: this scheme, we validate if temporal ordering knowledge of LLMMs could be directly used to estimate the causal estimand. We make use of templates shown in the App. Fig. 7. The causal estimand is estimated via the difference in logit values when intervening over an event, i.c. does the predicted probability take into account the context of events not happening? Surprisingly, we found that temporal ordering does provide a suitable proxy for estimating causal strength between the events. We further extend this approximation incorporate the backdoor adjustments in the A.
Backdoor Adjustments: For the experiments with Language models, we apply the backdoor adjustment to estimate the causal estimand AM- App. Fig. 8 shows the prompt template used to determine the relationship between the events. The prompt template takes trajectory, 4,, that contains all the events till the event E1 in sequential order of occurrence, further, an added prompt determines the intervention (do(Ei) or do(-E))) and the causal estimand is estimated using the logit values associated with the predicted token. App. B, Algorithm 3 provides the designed scheme to compute unbiased causal estimands. We essentially flip the options and generate the scores associated with options 'A' and 'B' for increase and decrease, respectively (more details in the App. C.)
Table shows comparison between various design choices. We observe that when using LLMs for estimation, the backdoor adjustment increases the performance over the temporal estimation scheme by significant margin. The understanding of these activities generie, and LLMs provide suitable set of sequences when prompted to generate list of steps to complete the activity. For example, when prompted with 'Generate the sequential steps a telegrammic style to perform the activity "going grocery shopping"' almost all the models we tested provide

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a valid set of steps for the given activity. However, when prompted with causal queries, the lower performance signifies the lack of understanding of the underlying causal mechanism. The constructed dataset helps to rigorously validate the understanding of the activity through an enormous number of causal query triplets. The results show that although the LLMS can explain the activity in detail, including generating correct steps for performing tasks, causally reasoning about the set of events remains challenging.
We conducted a small-scale human validation study over the created causal query dataset and asked graduate students to answer 100 randomly sampled causal query triplets (20 per activity). We record an average performance of 92.20%. (More details about the human study are provided in the App. B)
Human Study:
Related Work
Causal reasoning has been an active research area in the ML community [Spirtes al., 2000a, Peters et al., 2017, Schölkopf et al., 2021]. Some of the initial works highlight the causal nature of events present in text [Schank, 1975] as 'causal chains'. Multiple works have considered creating benchmarks/datasees that capture causal relationships between the events described in the text (see App. Table 5). More recently, with the rapid growth of LLMs on reasoning/understandiigg tasks, attention has shifted to validating these general-purpose models capturing causal reasoning [Jin al., 2023, Zečević et al., 2023, Willig al., 2023a, Liu et al., 2023, Willig et al., 2023b, Zhang et al., 2022a, Jin et al., 2024]. App. A.2 Table 5 shows a broad overview of the existing causal Dataset/Benchmarks presented in the NLP community. In this work, the primary focus is to bridge the gap between various lines of work that consider natural language to learn/ validatc/reason about causal relationships between events.
6 Limitations and Future Directions
One of the primary limitations of our work is the limited set of activities. Though the frameworks support generating exhaustive/enormous causal queries, finding general commonsense reasoning activities/tasks that are well understood by humans remains challenging. Moreover, creating a causal graph for an activity increases as we move toward more long-term tasks. However, as a general test of causal intelligence, our framework provides a suitable platform to validate the reasoning capabilities more rigorously. In the future, it would be interesting to sample trajectories from the observational distribution 9% to create a training dataset and check if causal reasoning ability can be acquired by language modeling objectives (including other variants like presented in Lampinen al. [2023]). We leave this detailed analysis for future endeavors. The proposed algorithm for causal triplet generation generates the simplest variant of causal queries in the form of causal triplets (also referred to as Pairwise Causal Discovery (PCD) task by [Chen et al., 2024]). More complicated causal queries can be generated, such as considering cases with common confounders, long/short causal chain dependency, etc. Moreover, taking formal definitions. (i.e., using the formal causal inference language) causal queries inspired from Jin et al. [2023, 2024] can be framed for a more rigorous analysis. Being at the initial state, we stick to the simple causal queries that provide two choices, and the task is to choose the more plausible cause. The creation of underlying causal graphs provides endless possibilities for creating varied versions of causal queries. In this work, we only consider unconditional version of d-separation. In the future, the same causal graphs could be used to define more datasets for covering other rungs of the 'causal ladder' [Pearl and Mackenzie, 2018].
7 Conclusion
In this paper, we proposed the COLD (Causal reasOning in eLosed Daily activities) framework for generating causal queries that can be used to rigorously evaluate LLMs. We performed extensive experimentation with LLMs for the task of Causal Commonsense Reasoning. Results indicate that LLMs are still far from complete understanding of daily commonsensical activities and fail to answer causal queries when analyzed in an exhaustive manner. We believe this framework will provide good platform for future research in understanding the causal reasoning abilitics of LLMs.
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Acknowledgments
We would like to thank the anonymous reviewers and the meta-reviewer for their insightful comments and suggestions. We would like to thank Google Deepmind India for helping us with the conference travel support.
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Appendix
Table of Contents
A COLD Framework Details
23
A.1 Adherence SUTVA
23
A.2 Comparison with previous Causal Reasoning Datasets/ Benchmarks
23
A.3 Observational Graphs
24
Algorithms in the COLD Framework
24
C Backdoor Adjustments
26
Experiments and Results
28
D.1 Compute Resources
28
D.2 Evaluation using Average Treatment Effect (ATE)
28
Prompt Templates for Language Model based Experiments
29
F Additional Results
30
F.1 Temporally Plausible Choices in Causal Triplets
30
List of Tables
5 Comparison with existing Causal Benchmarks/Datasses in NLP
24
6 Human performance
25
7 Results for Causal and Causal Temporal Triplets
32
List of Figures
4 Causal Graphical Model of Events. Ei temporally precedes E2, and is trajectory variable, which assumes a values where t E All trajectories from start to Ej
26
5 Input prompt formats for the MCQA-based evaluation of autoregressive ween-weight models
29
6 Qualitative examples for the MCQA-based evaluation of autoregressive open-weight models (e.g., (lama(-2), GPT-J, etc.).
30
7 Input prompt formats for estimation (temporal) via Language models.
30
8
Input prompt formats for computing the causal estimand using the autoregressive weint models (using backdoor criterion)
9
The "observational graph" for the activity Baking a Cake.
33
10
The "observational graph" for the activity Going Grocery Shopping.
34
11
The "observational graph" for the activity Going Train.
35
12
The "observational graph" for the activity Planting Tree.
36
13
The "observational graph" for the activity Riding on a Bus.
37
22

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A COLD Framework Details
The COLD framework consists of Observational Distributions represented in the form of DAGs (Go) along with the corresponding causal graphs (G.) governing the dependency of occurrence between the events. Table 2 highlights the total number of causal queries that can be created using the framework. Table 1 shows qualitative comparison with the COPA dataset Gordon et al. [2012] and the triplet samples coming from the COLD framework.
A.1 Adherence to SUTVA
In causal literature, a fundamentally acknowledged Stable Unit Treatment Value Assumption (SUTVA) [Cox, 1958, Rubin, 1980]) requires that for each unit (e.g. sequence of events), there is only one version of the non-treatment, i.e., for an event in the sequence, there lie only two versions occurring and not occurring. SUTVA plays vital role in causal inference by ensuring that each unit's treatment assignment has a consistent impact, facilitating the accurate estimation of treatment effects. Although, in the past, researchers have created various datasets that capture the causal relationship between real-world events [Gordon et al., 2012, Du et al., 2022], the problem of achieving the SUTVA assumption has remained challenging. For example, given events (taken from the COPA dataset [Gordon et al., 2012]) Fj: "The teacher assigned homework to students" and F.z: "The students groaned," " it becomes challenging to define =Eك since there are enormous possibilities that may have occurred at the same time (in place of E₁) that negates E1, making it difficult to define an event of not having done something. Recent work by Zhang et al. [2022b], proposes to use multiple alterations of events for capturing -E1, violating the SUTVA assumption. In this work, we highlight that if we define a closed system, capturing a commonsense activity, it facilitates adherence closely possible. For example, in the activity of "going via an
to SUTVA assumptions airplane, " one would have either "checked-in the luggage" (E,) or "skipped checking-in in luggage" due to smaller bags (-Fi). Moreover, developing causal setup with observations has always been a challenging problem in the wild and often requires few assumptions, as the strong causal link can only be established in ideal world where randomized controlled trials (RCT's are feasible. In our framework, adhering to SUTVA comes naturally where, in a trajectory, an occurrence of an event can be intervened obtain an alternate trajectory, reaching an ideal setup facilitating causal reasoning in daily commonsensical activities.
A.2 Comparison with previous Causal Reasoning Datasets/Benchmarks
Table 5 shows broad overview of the existing causal Dataset/Benchmarks presented in the NLP community. We find that most of the existing set of work relies on real-world events to reason about causality in NLP, where human annotators are asked reason causally between the nature of events. However, most of these datasets/benchmarks try to establish a connection using a simple question prompt, which may not be enough to construct the underlying causal graph. Moreover, most of the real-world grounding- -based methods remain open-ended due to the events taking place in the wild, making it difficult consider constructing a causal graph where multiple variables play role. More recently, with increased research attention on the causal reasoning abilities of LLMs, researchers have tried framing causal queries based on a causal inference engine, requiring the underlying causal graphs. However, when constructing causal queries from prompting LLMs natural language is used to verbalize the causal concepts in the form of symbolic variables that may not have a real-world grounding.2 Moreover, the created causal queries are difficult for human with little or no knowledge of causal inference concepts. 3 Table 5 shows comparison of all these features in detail, where COLD satisfies all the features.
We realize this a first-of-it--kndd framework built over real-world events and contains the underlying causal graph. Having both the Observational Distribution (representing the enormous event sequences present daily activity) and the manually created underlying causal graph helps facilitate an in in-depth analysis of the causal reasoning abilities of LLMs. Moreover, the same framework can further be extended in various ways: 1) Extending the number of activities: In the current version of the framework, we only consider 5 daily activities to provide an in-depth analysis. In the future, those can be extended to incorporate more such activities. 2) Extending the scope of activities: The tasks
23

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Table 5: Comparison of causal experimental settings used in prior LLM evaluation benchmarks. The
real-world grounding plays a crucial role evaluating LLMs, which is not present in symbolic benchmarks.
Datasets/eachmarks
Real-World
Causal Graph
Symbolic
Exhaustive
# Samples
SemEval2021 Task8 [Hendricks et al., 2010]
X
1331
EvenCuusallty [Do et al. 2011]
COPA [Gordon al.. 2012]
1000
Causal TineBank [Mizza et al., 2014]
X
CaTeRS [Mostufazadeh
2016]
320 stories (1.6K sent)
BECauSE [Dunictz et al., 2017]
X
X
Mied [Rashkin
2018]
25K event phraees 877K
ATOMIC [Sap et 2019a]
X
X
SocialIQA ISap 20196]
TimeTravel [Qin et 2019]
81.4K
Abductive (ART) (Bhagavatula al., 2020]
X
X
20K narr. 200K expl
Com2Sense [Singh et 2021]
4k pairs, 30K questions 274 PCT 20K CR questions 1.1 Million 1,360 event pairs 2.7K pains
TeylMeWhy [Lal 2021] 2021]
CRASS [Frohberg Binder. 2022]
e-CARE [Du al,, 2022]
CausalQA [Bondareako et al. 2022]
COLA [Wang al., 2023]
CRAB [Romamon et al., 2023]
CausalJudgement (Srivastava
2023]
7
Cor2Cause [Jin al., 2023)
200K
CausalParrots [Zešević al., 2023]
Cladder [Jin al., 2024]
10K
COLD (ours)
~9.52 Million
used in the activities are generic and capture commonsense; for validating domain-specine causal reasoning abilities, the framework could be extended to domain activities, for example, cooking specific recipe where adding different ingredients causes variation in taste. 3) Extending the type of causal queries: while constructing the causal queries, we considered the simplest task finding the more plausible cause/effect given two options events, keeping only unconditional d -eparation as the primary condition. The framework can directly be extended, keeping causal queries inspired from Jin et al. [2023, 2024].
On the analysis front, realize that the possibilities of in-depth analysis increase significant margin. In this work, we shed light on few mechanisms for validating causal reasoning abilities via zero-shot CCR (compared previous works that rely training and further testing on similar datasets). We specifically focused on open- openhweight models for better applicability in the future and proposed a few mechanisms for estimating the causal relationships between the events. This opens up several possible avenues for an in-depth analysis of LLMs.
A.3 Observational Graphs
Fig. 9, Fig. 10, Fig. 11, Fig. 12, and Fig. 13 shows the "observational graphs" for the activity Baking Cake, Going Grocery Shopping, Going on a Train, Planting a Tree, and Riding Bus respectively.
B Algorithms in the COLD Framework
In this section, we provide insights into the Algorithms used in the COLD framework. We start with Algorithm which creates causal query triplets given the observational graphs G. and along with the Causal Graphs G.-
Temporal precedence is generally assumed essential for defining causation, and it is one of the most important clues that is used to distinguish causal from other types of associations [Mill, 1898, Hill, 1965, Pearl and Verma, 1995]. For framework, sorted order obtained from the observational graphs and use the temporal order to define the causal query triplets, i.e., the cause events will always precede the effect events.
Remark:
also consider the topologically
Creating Causal Query Triplets: The Algorithm
designed to sample all the possible causal query triplets to construct a dataset for validating causal reasoning ability over activity. Provided the observational graphs G. and the Causal Graphs G. for an activity, we first sample all the possible node triplets in the graph. Later, iterate over the set of triplets and check if one of the nodes in

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Table 6: Human validation done for small sample of 100 causal query triplets. Overall we find
that humans do perform well in causal reasoning about these daily activities.
Human Annotators
cake
shopping
train
tree
bus
Average
Subject
95
95
90
100
90
94
Subject
100
100
90
95
90
95
Subject
100
100
85
85
70
88
Subject 4
100
100
95
90
85
94
Subject 5
100
85
95
90
80
90
Average
99.00
96.00
91.00
92.00
83.00
92.20
the triplet (n,, ny, n&) is d-separated. Further, the d-separated node becomes the wrong choice. The remaining two events become premise and correct choices, depending on their temporal order. For example, if n, is the node that is d-separated from n, and n2g, we check if ny and nig have a causal link between them in Go- If nj and ny are found have causal link, create two triplets using the temporal ordering between ni and fig. The temporal link n2 + Th leads to an 'effect' query where nj becomes 'premise' and ny becomes 'correct choice,' and 'cause' query where ny becomes 'correct choice" and vi becomes the 'premise." Note in Algorithm 1 [Store tuple]. we only show one such instance for brevity; the implementation will consist of another mirror instance (i.e., for every [Store tuple] both 'cause" and 'effect' question triplets are stored to the dataset).
The understanding of these activities is generic, and LLMs do provide suitable set of sequences when prompted to generate a list of steps to complete the activity. The constructed dataset helps to rigorously validate the understanding of the activity through an enormous number of causal query triplets. The results show that although the LLMs can explain the activity in detail, including generating correct steps for performing tasks, causally reasoning about the set of events remains challenging.
Human validation: To get rough estimate of the human performance on the created causal reasoning queries, we also perform a small-scale human study, where the annotators are given a set of randomly chosen 100 causal queries. The human subjects were graduate students of computer science who were given brief tutorial about counterfactual reasoning. Table 6 shows the obtained results. We would like mention that validating human performance is challenging due to the nature of the causal reasoning task. The nature of counterfactual reasoning requires the human/algorithm to assume a similar alternate world/universe with only particular happening or not happening to approximate the causal strength between the events. These imaginations can be expressed in statements as highlighted by Pearl and Mackenzie [2018], Pearl al. [2016], containing an "if" statement in which the "if" portion is untrue or unrealized (aka counterfactual). The "if" portion of a counterfactual is called the hypothetical condition, or more often, the antecedent, making it challenging (cognitively heavy) to conduct human evaluation. Please note that the study is performed using only a small sample of 100 causal query triplets out of thousands of queries, and the presented results only provide rough estimate that may not generalize for a larger number of queries. Hence, a comparison of human study results and LLMs is not fair, and the presented human performance estimates may not be true representative of the entire population. Interactions with human subjects also revealed that they tend to confuse temporality and causality (similar findings were reported by Do al. [2011]).
Evaluating over Causal Query Triplets: Algorithm 2 makes use of the causal estimands A to compare the causal strength between the premise event and the choice events. We consider the causal estimand computed between the premise and the available set of choices and predict the label corresponding to the high A values. For given causal query from the created causal query triplet dataset D, where each data entry D. corresponds to (p, C1, C2 9. l), i.c., premise event, choice 1, choice 2, question and the label respectively. As the task is to predict the more plausible cause/effect of the given premise event, we create two event pairs, (p, (1) and (p, c2). and compute the causal estimand A for both the pairs using the temporal or the backdoor scheme (described below in Algorithm 3). Note that the order of events given to AM is in E1 and Eg format, i.e. AM(E,, Ez). Using the temporal precedence (highlighted as remark above), the cause event will always precede the effect event temporally. Hence, for a causal query with the question as 'cause', the causal estimand estimated as A M (ci,p')) AM (ch. p²) and A (p²,,, cf), (p'r", .c)) when the question is 'effect.' Further, based on the estimated AM scores, the more plausible cause/cffect is predicted.
25

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Algorithm Creating Causal Query Triplets
Ge: Causal Graphical Model; Go: Observational Graph;
d,GG,x, X.G.X, y): True iff (x. y) d-separated unconditionally in any DAG G:
de(G, y): True iff y) are d-connected unconditionally in any DAG
genSamples GG): Generates all possible node triplets (n4, f1,, reg) from DAG G such that و
where (f. k) are the respective indices of nodes in topologically sorted list of nodes;
AG,,,, y)): True iff x is ancestor of y in DAG G.
@:: Exclusive OR operator,
p: premise; c1: choice I; ez: choice q: question; I: answer (label)
(p. c1. c2.4.
D=
[Emply Dataset] [Generate Samples]
S~ genSamples(Co) for (n., Ry, nu)) in S
if (G, ni, n2) then
de (Ge, ns) @ de (y. mk)
"cause"
arg meg ne; mk), de ny, nz]]
p. Cı, C2 t ne. nl, ny
if (S,.,, then APPEND(D, (s..
[Store tuple]
else if (Ge, me) then
if de (9:, n2) @ de(Se, m.,
Q 'effect'
= arg max ma ns, mk), de(Ge, ni, n;)]
P. C2 < ni, ni
if (G..P. et) then APPEND(D, (p,c,,
[Store tuple]
return D
Computing AM: Algorithm depicts the process computing an unbiased estimate for the causal estimand. The causal strength is computed between two events E₁ and Ez where E₁ is assumed to be preceding Eg temporally. To make an unbiased estimate based on the provided options, we consider normalizing the obtained probability scores by flipping the options and providing the same query prompt to the Language Model.
SM (Fi Fe, @) SM Fr, F2, EM, + SMM E2, Φ) + SM E,, Φ1) + 3. (E, E2,, + (M, E2, ob))
where ø denotes the prompt template as shown in Figure 8 (top) and o, denotes the same prompt with flipped options, Figure 8 (bottom). The overall equation helps normalize the prediction probabilities of the 'Increase' option by using the probabilities of the 'Decrease' option. Finally, these normalized scores are computed for multiple trajectories tę in the backdoor adjustment scheme compute the causal estimands pM E2 do( E1)) and pm [E2 do(-E)) that help estimate the causal strength M between events E1 and Ez.
E
E2
Figure Causal Graphical Model Events. Eg temporally precedes F.z, and trajectory variable, which assumes values t where & All trajectories from start to Ej
C Backdoor Adjustments
set of variables W satisfies the backdoor criterion relative to 7 and Y if the following are true
(A) W blocks all backdoor paths from to i.e. blocking confounding or non-causation association paths
(B) W doesn't contain any descendants o
26

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Algorithm 2 Evaluating Causal Query Triplets
Th: unique trajectories from Start node (start of the activity) to node Ei
P.((Esdd(EE))
7m E
E 1)
P.n(Esdd(-Esi) =
P( =1
[7x
AM: Returns Average treatment effect (pM Ea (do(())) Bi)) pa<(Padd((-Bi)) to determine the causal
effect of event Ej on event Eg
premise; ct: choice I; c2: choice 2: question; I: label
D: Set of all causal queries
Dj: Causal query (p, ct, C2, 9. (), where (p, e1 02,00, D
for D. D do
if == "cause" then
prediction t arg max [Am(ci))) Am((s,)) else if q- == 'effect' then prediction + arg max AAm(D et), Am(p', cb)
end if
7) 1) + (prediction == 4;)
end
return D/ D
Algorithm 3 Computing Causal Estimand
E,, E2 Events in an given activity
To Set of trajectories (temporally ordered sequence of events)
SM E2, Φ) score of token A (associated with option 'increase") using prompt o of model M.
(E1, E2, Φ t score of token (associated with option 'increase") prompt øy (flipped options in prompt (6) of model M.
&. (11, F2, .) &- score of token B (associated with option 'decrease") using prompt 6 of model M. (M F2, $5)) 4- score of token A (associated with option 'decrease') in prompt Øy (flipped options in prompt o)of model .M
SMi Ez, Φ) + (M E2, SM SM E2, E2, + (Mi Ez, E2,0) 3 (Ei, ky)
Norm. Score [M (2) <-
E(E E1, = () + Sm((l, E,) E2,
E2, +)
p(Eg E1, - [m(ll, -E1, E2.
Pm (M((gldo(Eii))= Ta EIET, (1, pm (E(B)ldo(EEii) (.. Eier, B(E22-EE1, t) AM + pm (Ea]do(Ei)) (Ei)) pM((E))d((EEi)) return AM
Then, W satisfies the backdoor criterion [Pearl et al., 2016, Neal, 2020].
Adhering to the above conditions of the backdoor criterion, it is reasonable to assume that the trajectory (temporally ordered sequence of events) till E; will contain the events that confound the event E₁ and event Fez (condition A). Every event trajectory till Fg will temporally precede En (condition B). Hence, the trajectory variable will satisfy backdoor criteria in the proposed closed system. The domain of the trajectory variable is set of all trajectories till Fj. Therefore, conditioning on t closes all paths that induce non-causal associations. The generic representation of approximate causal graphical model involving E,, Ez, and is shown in Figure 4, and can be formulated as:
P:M(Eydo(E:))
1 [T]
Σ ((E2|E,, 2 =t)
LT
Where T is set of all trajectories from the start of the activity till the event (excluding E1).
27

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D Experiments and Results
D.1
Compute Resources
We perform all the experiments using machine with 5 NVIDIA A 100 GPUs. We use only the open-weight models with frozen parameters to present the results for better reproducibility in the future.
D.2 Evaluation using Average Treatment Effect (ATE)
Establishing Causal Relationships: To validate the causal reasoning ability, the MCQA- approach can be further extended to estimate the causal estimation and denote the causal strengths between the events. Establishing cause-and- -nffffet relationships can be achieved through various statistical analyses. The strength of couse-and-effee relationships is approximated by statistically analyzing events' behavior using observational data (PC-Algorithm, Spirtes et al. [2000b]). Moreover, some of the recent works [Wang et al., 2023] highlight the role of context in determining the causal relationships between the events. To extend our analysis of causal reasoning abilities in the proposed framework, we use the backdoor adjustments in LLMs as explained in the main paper. Moreover, we also perform an interesting analysis of the observational graphs for estimating A statistically.
1) Through Original Trajectories: DeScript Wanzare et al. [2016] collects data by considering 100 ESDs written by different crowd-sourced workers. We use the original Trajectories (ESDs) To written by humans present in the DeScript dataset. These ESDs provide the original flow in the graph directly coming from crowdsourced workers. We consider these as the original trajectories To Applying the backdoor criterion (Eq. 2) over these trajectories To An interventional distribution similar to the previous section is computed considering the likelihood of occurrence of Eg under each treatment (Ei and -Ei) for only these trajectories To These estimations are further used to compute the treatment effect using the Eq. 1. We denote the causal risk difference (A) computed with To as Ao.
2) Through Observational Graphs: The observational graphs provide a proxy for the underlying knowledge about the activity, covering all possible sets of events, i.e., starting from the start node, one can trace multiple trajectories that will essentially define the way of performing the activity. For every pair of connected events (e;, Cy), the edge between them represents the probable transition from e, to ej with some non-zero probability. However, a noteworthy point is that the transition probability between two connected events (e;, eÿ) can vary depending on the design choices/transition function T(e;, Eÿ) - (0, 1]. We define this transition function two ways: 1) Uniform Node Transition (T,): The transition probability from current node to next probable events éj Eij would be uniform that is Tes,,e) = J.. where |o,| represents number of outgoing edges from event e, (i.e., assuming after an event, the choice of the next event uniform from the possible events). 2) Uniform Trajectory Transition (T,): Another way to take the set of events in an activity (trajectory) is by considering all the possible paths being equally probable, i.e., across the entire population the same activity will be represented with one of the possible trajectories. Hence we can define the transition function with each trajectory = (Estarts E2 ... Gend) (sequence of events from starting ending) having the same probability, i.c.: p(t.)= = p(t,) ET p(t)) = II 1.mtt
Tílei em)
Further, given transition function T, computation of E(E2)E1. = t.) becomes straightforward as p(Eg Er), ) since the next course of trajectories after En will be decided given E₁ has occurred. Analytically, it can be computed by counting every trajectory (i.e., Tij) from Eg that leads to Es
p(Eb)E))= = Σ
II
T(e: + em)
(4)
TET iimt
Tk= M
((E = Σ th
(E, + Ez)
(5)
km
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For estimating the probability p(Eg)-E1, t.), we make use of the observational graph by considering all the parent nodes of E₁ and compute the probability of reaching Es from the parent (i.e., last event of trajectory 14) avoiding the occurrence of E1.
p( F1, 4,)
E T(e: + emm)
(6)
BET LiEt Ei
also, p== = -4) be computed as product each transition in 4, i.
p(= = 1.) II T(e: +t,
Computations from Equations 5, 6, and are used in the backdoor adjustment defined in Equation 2 for estimating the interventional likelihood of occurrence of Eg under each treatment (E₃ and F11) and causal risk difference (A). Note depending on the choice of transition function (Tn or Tj), we obtain two deltas A, and A,, respectively.
E Prompt Templates for Language Model based Experiments
We present the various prompt templates used to estimate the temporal link between the events in Figure For BERT-based models, we use the MLM-trained models for predicting the masked token given a sentence (Previously, similar approach was adopted by Zhang et al. [2022b]). In contrast, autoregressive models, we frame the prompt as a question-answe objective, taking inspiration question is framed to predict
from [Robinson and Wingate, 2023], where multiple-choice-basse the answer the form of the option IDs. The prompt intentionally structured so that the LLM intended to predict single token (Such as "A", "B", etc.). Robinson and Wingate [2023] highlights the advantages of MCQA-based evaluation over cloze evaluation (where the LLMs are expected to generate the entire answer in cloze test), leading significant boost in various tasks, including commonsense-basee tasks.
For our prompt-based evaluation experiments over the generated causal triplets, we follow the same MCQA- -bssed strategy and frame the prompts accordingly for fair evaluation. Figure 5 presents various prompt templates for autoregressive experiments, and Figure 6 shows a few qualitative examples for the framed causal query templates.
Consider the activity of activity name.
in-context examples (if few-shot/in-conter learning experiment) ]
Which of the following events (given options B) is plausible question (cause/effect) of the event premise?
A. choicel
B. choice2
Answer:
The following multiple choice questions about activity name. You should directly answer the question by choosing the correct option.
[ in-context examples (if few-shot/in-contxtt learning experiment)
Which of the following events (given as options A B) is plausible question (cause/effect) of the event premise? A. choicel
B. choice2
Answer:
Figure 5: Input prompt formats for the MCQA-based evaluation of autoregressive open-weight models (c.g.. llama(-2), GPT-J, etc.). The black text is the templated input. The orange text is the input from the created causal query triplcts, where the activity name denotes the description of the activity like baking a cake. The next-token prediction probabilities of the option IDs at the red text is used as the observed prediction distribution.
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Consider the activity of baking a cake. Which of the following events (given as options A B) is plausible effect of the event preheat oven to 350 degrees.?
A. turn off oven.
B. prepare the microwave oven and required utensils
Answer: A
The following are multiple choice questions about going on train. You should directly answer the question by choosing the correct option. Which of the following events (given as options A or B) is plausible cause of the event get the bill for groceries. ?
A. pay the cashier for your items.
B. place cart into cart corral. Answer:
Figure 6: Qualitative examples for the MCQA-based evaluation of autoregressive open ween-
models (e.g., 1lama(-2), GPT-J,
In terms of 'before' and 'after', the event: "first event text" would have happened <mask_token>> the event: "second event text"
Consider the activity of activity name.
Question: Determine the temporal order.
following events took place: 1. first event text, second event text the first event occur 'before' or 'after' the second event? (choose from the given options)
before after
Answer:
Figure 7: Input prompt formats for the A estimation via Language models. The first row shows the prompt template used for BERT-based language models, where the mask token is predicted. The second row shows the template for autoregressive open-weight models (c.g., 1lama (-2), GPT-J, etc.). The black text the templated input. The orange text is the input from the created causal query triplets, where the first event text and second event text comes from the premise and available set of choices. The mask-token prediction probabilities of 'before' and 'after' and token prediction probabilities of the option IDs at the red text are used as the observed prediction distribution for BERT-based and GPT-based open- open-weight models.
Additional Results
F.1
Temporally Plausible Choices in Causal Triplets
Some of the initial studies [Do et al., 2011] highlight the difficulty choosing between the causal- effect events and temporal events (that occur in close proximity to the premise event), i.e., temporal relationships are sometimes considered as a causal relationship by human annotators. We also create another version of created causal triplets where the wrong choices are replaced by temporally near nodes (nodes that are at a one-hop distance from the premise node). We call these 'causally hard triplets.' Note the temporal nodes obtained from the observational graphs G.. Table shows the performance comparison with causal triplets and sausal-tempora triplets versions of the same queries. We observe a significant performance drop on the causal-tempon triplets version for most models, highlighting the increased confusion.
30

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CAUSAL REASONING ANALYSIS:
Context: For the activity activity name. During the activity, the following set of sequences occurred in order:
ordered list of events present in Trajectory till Ej
Trajectory (: = T
Further, the event 'event text for E took place.
# Intervention (do Eg
Question: Given the above information, will the chances of the occurrence of the event 'event text for H/2 increase or decrease!
A. Increase
B. Decrease
Answer:
(pl Eg [r) T
CAUSAL REASONING ANALYSIS:
Context: For the activity activity name. During the activity, the following set of sequences occurred in order:
ordered list of events present in Trajectory till Ail Further, the event 'event text for Ej did NOT take place. Given the above information, will the chances of the occurrence of the event 'event text for Bg' increase or decrease?
Trajectory (7))
# Intervention (do(-EE)
A. Increase
B. Decrease
Answer: B
(p(E2 do(-Ez); T
Flipped options variant of the above Prompt Template
CAUSAL REASONING ANALYSIS:
Context: For the activity activity name. During the activity, the following set of
sequences occurred in order:
orderedd list of events present in Trajectory till E,] Further, the event 'event text for Ej' took place.
Trajectory (: T
# Intervention (dol E,
Given the above information, will the chances of the occurrence of the event *event text for Eg" increase or decrease?
A. Decrease
B. Increase
Answer:
(p(Eg dol E)(E)) TD
CAUSAL REASONING ANALYSIS:
Context: For the activity activity name. During the activity, the following set of sequences occurred in order:
[ ordered list of events present in Trajectory till Ej]
# Trajectory (T)
Further, the event 'event text for E did NOT take place. Given the above information, will the chances of the occurrence of the event 'event text for Es increase or decrease?
# Intervention (do(-Ei)
A. Decrease
B. Increase Answer:
(p(Eg dol E,), T
Figure 8: Input prompt formats for computing the causal estimand using the autoregressive open- weight models (using backdoor criterion) (e.g., 1lama(-2), GPT-J, etc.). The black text is the templated input. The orange text the input from the created causal query triplets, where the cake. The trajectory Tr is
activity name denotes the description of the activity like baking obtained using the observational graph Go and contains the sequence of events before the event E, The next-token prediction probabilities of the option IDs at the red text is used as the observed prediction distribution. The flipped options variants of the prompts contain the same query with flipped options (i.e., the option 'Increase" becomes 'Decrease' and vice versa). This is done to make the causal estimand unbiased towards the predicted option token as highlighted Algorithm
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Table 7: The table provides evaluation results of Language models over the causal and causal temporal triplets.
Triplets
Model Name
cake
shopping
train
tree
bus
gpt-neo-125M
50.71
50.01
49.99
50.13
50.15
1ot-nee-113B
44.77
45.69
42.52
45.67
42.89
gemma-2b
53.76
52.19
60.57
60.71
53.64
causal triplets
gpt-neo-2.7B
50.00
50.01
50.00
50.01
50.00
phi-2
85.14
83.65
77.29
82.24
71.74
gpt-j-6B
49.59
50.02
50.29
49.92
49.93
Llama-2-7b-chat-hf
77.92
72.41
73.48
72.40
68.21
Mistral-7B-v0.1
77.64
69.38
68.46
72.43
69.37
gemma-7b
81.47
82.26
77.24
80.78
70.29
Meta-Llama-3-8B
80.79
76.46
76.08
78.21
67.39
gpt-neo- 125
50.60
49.80
49.90
50.00
50.20
causally hard triplets
1ot-nco-1.3B
49.50
51.20
48.80
47.50
48.00
gemma-2b
52.30
51.00
56.10
52.20
50.00
gpt-neo-2.7B
50.00
50.00
50.00
50.00
50.00
phi-2
80.00
74.70
67.90
87.50
gpt-j-6B
50.20
50.00
50.30
50.00
49.80
Llama-2-7b-cae
71.60
66.60
68.30
77.00
65.40
Mistral-7B 10.1
69.20
63.10
64.20
67.90
62.00
gemma-7b
76.30
76.40
69.70
89.70
63.70
Mcta-LJama-3-88
77.30
72.20
69.30
83.20
64.30
32

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choose recipe
buy ingredienesss
other return-hore
Other-enter- enter-kichhenen
Ether-reat instructions
get inn-ieigredents
get-utensils
get-naasuring-utenssll
preheat
neasure- Lngeedenng
add-ingredients
Saher-nake-col
rengere-ingredeents
grease-cake-tin
pour dough
cake
time
other-clean
other-
check
-ther-Dake-extree time
turn-off-oven
cowl-down
take-out Sete--Outcakk---e cake-tin
decorate
End Task
Figure 9: The figure shows the "observational graph" " for the activity Baking Cake.
33

---

make- List
take-bags
onher-turn-car-on
carry-cca
other-get-in--o
go-grocery-drive
other-park-car
00- www.ggroorryyyoo
go w-g.rrccrrbbii
get- get-list-fron-cm
enter
take-shop shop-cart
other-shop
nove- section
get-groceries
check list
go- jo-checkout
other-checkout
conveyor
theer-presentccouposs
cashier scan/weight
other- get- total
other get wallet
pay
pack- grocerrossiiss
rec-ip
other cotte--put-ie put-bags--eee begs-cart
other- take-bags
bring vrinclvehicle
place- cato-cart-corral
set-itens In in-kitchen
End Task
Figure 10: The figure shows the "observational graph" for the activity Going Grocery Shopping.
34

---

decide-where- to
check- time- table
other-pack
other-get-in-car
get- Eet-train-station
other cark--
enter- station
gettr-get-out-car
other go ticket counter
other-waid queue
get- tickess
other- finer-find-oe fond--innndfeee platform
get-platform
other-walk
wait
train-arrives
sther-wait-to--ae to-bard
-et-on on
other-stow- luggage
Lind-place
conductor-check
spend time train
arrive-des lelive-destinattoon
get-off
other take luggage
other Srave-leave-staeee station
End Task
Figure 11: The figure shows the "observational graph" for the activity Going on a Train.
35

---

other garden center
choose-tree tree
get-tree
take-hot hone
Tind-place Find place
get tools
other-pur on--oongl
dug-hole
other-secure-
fict-feetilizers
other-check-hole
unwrap-root
other get--eet---llsss
place-
other-hold-tree
refill-hole
other-check-sabillity
tamp-dirt
other- pet--puut-u-cccc nulch
other-put paarrrppputwaaa----e away-too to
water
fle-stakes-up up
End Task
Figure 12: The figure shows the "observational graph" for the activity Planting Tree.
36

---

other- cost
check time- table
(ind-bus
other-decide
get-bus- stop
wait
other- signal- -driver
bus comes
other- other-driver- orens door
board- bus
get get-tice ticket
find place
ride
spend time- bus
stop- cop-buttoon
go-exit
other- reach
other-thank
stops
gef-off
End Task
Figure 13: The figure shows the "observational graph" for the activity Riding on Bus.
37

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Reviews:
[{"review_id": "kybR6Bu1nQ", "reviewer": "Reviewer_iBzV", "summary": "This paper presents a new causal reasoning dataset for LLMs. The main motivation of the dataset is to bridge the gap between casual commonsense reasoning datasets and symbolic representation-based causal reasoning datasets. Specifically, the proposed dataset collects crowd-sourcing observations, and constructs related causal graphs and triplets. This paper evaluates multiple open-source LLMs using the collected datasets, benchmarking LLM's ability to predict causal relationships, and to estimate average treatment effect.", "questions": "1. How sensitive are the eval results w.r.t. the prompts? I'm a bit concerned that since \"cause\" and \"effect\" are not common words (especially their formal definition in causal inference), the model's ability may be underestimated with these prompts.\n\n2. There are multiple different methods to estimate probability prediction from LLMs? Have you tried other methods and will that change the empirical results significantly?", "limitations": "Limitations are discussed in Sec. 6.", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The construction of the causal graph and the estimation of casual relationships are strict and sound. The whole dataset provides high-quality causal relationship annotations for real-life events.\n\n2. A large number of open-source models are evaluated. The evaluation on ATE estimation also involves multiple estimation methods using LLMs.", "weaknesses": "1. Human performance is not provided in the empirical comparisons. This makes it hard to understand LLM's performances. One can argue that the performances are far from perfect. However, due to the inherent ambiguity of natural languages, and inherent disagreement in people's opinions, I would assume human performance will also be significantly lower than 100%. Adding human performance in these tables will make the comparisons more informative.\n\n2. Many important details are in the appendix. I understand this is a dense paper with lots of content, however, the presentation and organization can be greatly improved. Additionally, the current results section includes a significant part on how to estimate ATE, which should be included in previous sections.\n\n3. While the dataset itself is huge, there are only five different events (shown in Table 2). Therefore, it is unclear how representative the model's performance on this dataset will be. This is a significant limitation, especially due to the \"Causal Parrots\" phenomenon mentioned in the introduction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your detailed and insightful response. Please find the response to the mentioned weakness/clarifications below:\n\n* **Lack of Human Performance:**  We would like to mention that validating human performance is a little challenging due to the nature of the causal reasoning task. The nature of counterfactual reasoning requires the human/algorithm to assume a similar alternate world/universe with only a particular happening or not happening to approximate the causal strength between the events. These imaginations can be expressed in statements as highlighted by Pearl et. al, containing an “if” statement in which the “if” portion is untrue or unrealized, which is known as a counterfactual. The “if” portion of a counterfactual is called the hypothetical condition, or more often, the antecedent, making it challenging (cognitively heavy) to conduct a human evaluation. We agree that human performance may be lower than complete perfection and adding human performance in these tables will make the comparisons more informative. However, the true dependency of the events is coming from the underlying causal graph, making the generated causal queries accurate. Previously, works like Jin et. al have released causal queries (https://huggingface.co/datasets/causalnlp/corr2cause, https://huggingface.co/datasets/causalnlp/CLadder) generated from the underlying causal graph that does require causal inference theory understanding to be purely annotated by humans.  \n\n* **Presentation and Organization of the paper:** We agree that the density of contents in the paper is a little high, and the presentation can be improved by a significant margin. We agree that it would be better to keep the estimation of ATE as a separate section, however, due to limited space, we had to merge the experiments and the results section (Section 4 Experiments and Results). We hope you understand that fitting all the details in the main paper is challenging, we would love to have some feedback if you think any particular section would be better suitable to move in the main paper from the appendix. We will make the suggested changes in the camera-ready version of the paper. \n* We agree that one of the limitations of our work is the limited set of activities, we also highlight the same in the limitation section of our paper with suitable reasons in [Line 349-355]. We would like to reiterate that finding general commonsense reasoning aactivities/tasks that are well understood by humans remains challenging in general and it would be good to explore in the future by adding more set of real-world activities. Moreover, creating a causal graph for an activity increases as we move toward more long-term tasks. However, as a general test of causal intelligence, our framework provides a suitable platform to validate the reasoning capabilities more rigorously, which is missing from the current literature. We hope, with the enormous causal queries (simulating/coming close to the mini-turing test), the created dataset will serve as a robust testbench for evaluating causal reasoning in LLMs. \n\n\n**Response to Questions/Clarifications:**\n\n**Q1)**  In general, LLMs are found to be sensitive toward prompts, and coming up with a suitable prompt may affect the performance. Previously, various LLM benchmarking papers have performed evaluation over causal datasets like COPA, CausalBench, etc.  LLMs are few-shot learners ROCK has benchmarked LLMs using the cloze evaluation or the MCQ evaluation. We consider the latter that was also justified for benchmarking commonsense reasoning activities in general. \n\n**Q2)** We agree that there are multiple ways of estimating probability predictions. Some of the widely accepted ones include the CLOZE test evaluation and the MCQ-based evaluation. As highlighted in the paper, we use multi-choice question-answering (MCQA) [Robinson and Wingate, 2023]. Robinson and Wingate [2023] highlight the advantages of MCQ-based evaluation over cloze evaluation [Brown et al., 2020] (where the LLMs are expected to generate the entire answer in a cloze test), leading to a significant boost in various tasks, including commonsense-based tasks. We agree that there is a risk of estimating bias in probability that may result in variance in prediction results.\n\nTo eliminate the bias in the probability predictions, we consider the averaging  (similar to https://openreview.net/forum?id=shr9PXz7T0) that uses multiple permutations to balance out the prediction evaluation.\nAlgorithm 3 in the Appendix depicts the process of computing an unbiased estimate for the causal estimand. The causal strength is computed between two events $E_1$ and $E_2$ where $E_1$ is assumed to be preceding $E_2$ temporally. To make an unbiased estimate based on the provided options, we consider normalizing the obtained probability scores by flipping the options and providing the same query prompt to the Language Model.\n\n$$\nf_M(E_1,E_2,\\phi) \\leftarrow \\frac{s_M(E_1,E_2,\\phi)+s_M(E_1,E_2,\\phi_f)}{s_M(E_1,E_2,\\phi)+s_M(E_1,E_2,\\phi_f)+\\tilde{s}_M(E_1,E_2,\\phi)+\\tilde{s}_M(E_1,E_2,\\phi_f)}\n$$\n\nwhere $\\phi$ denotes the prompt template as shown in Figure 8 (top) and $\\phi_f$ denotes the same prompt with flipped options, Figure 8 (bottom). The overall equation helps normalize the prediction probabilities of the 'Increase' option by using the probabilities of the 'Decrease' option. Finally, these normalized scores are computed for multiple trajectories $t_i$ in the backdoor adjustment scheme to compute the causal estimands $p_\\mathcal{M}(E2 \\mid do(E_1))$ and $p_\\mathcal{M}(E2 \\mid do(\\neg E_1))$ that help estimate the causal strength $\\Delta_{\\mathcal{M}}$ between the events $E_1$ and $E_2$.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UIj94U4nRZ", "reviewer": "Reviewer_G7bs", "summary": "The paper proposes a dataset for evaluating the causal reasoning capabilities of LLMs by grounding evaluation in human understanding of real-world daily activities. The authors address the gap between open-ended causal commonsense reasoning and symbolic question answering by introducing the COLD framework, which generates causal queries based on daily activities. The paper describes the creation of causal graphs through crowd-sourcing. The authors test different LLMs using these causal queries, and show that even simple causal reasoning tasks remain challenging.", "questions": "1. What specific insights have you gained from your experiments with this dataset? Can you elaborate on how these insights contribute to our understanding of the causal reasoning capabilities of LLMs? Could we use it to propose any improvements to the current models?\n\n2. Are there plans to incorporate more traditional causal inference use-cases, such as treatment effect estimation or causal graph discovery, into your framework? I mean this in the sense of evaluating the capabilities of LLM in performing these tasks.\n\n3. In the first example in Figure 3, both options appear plausible without additional context. Can you provide a detailed explanation of how the correct choice is determined in such cases, and whether there is a mechanism to ensure that the queries are unambiguous?", "limitations": "The authors acknowledge limitations of their work, such as the restricted set of activities and the challenge of creating causal graphs for more complex long-term tasks.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Its approach to evaluating causal reasoning in LLMs by grounding the evaluation in real-world activities. \n\n2. The use of a large dataset to create extensive causal queries.\n\n3. The detailed experimentation with various open-source LLMs and the plan to publicly release the framework and datasets.", "weaknesses": "1. The data used in the study appears overly simplistic, focusing on basic daily activities which may not provide a robust test for causal reasoning. If the goal is to evaluate how well models understand causal reasoning I would stay close to how we perform causal inference in science (i.e. gather data, understand whether we have an identification strategy, compute treatment effects/learn causal graph).\n\n2. Causal Commonsense Reasoning is not causal in the sense of statistical causality. Almost all uses of causality in science are either: 1. To estimate a treatment effect in the real-world 2. To discover a causal graph, again in the real-world. This is done either from interventional or from observational data. This data doesn’t capture any of this, so I wonder what the usefulness of it is in relation to the causality literature. \n\n3. The insights drawn from the experiments are not clearly articulated. What exactly has been learned from this new extensive dataset and all of the experiments? \nThe ATE experiments are particularly hard to understand. ATE measures the marginal effect of a treatment on an outcome. Comparatively, this dataset includes binary questions about causal triplets. In the results the authors show accuracy measures, I don’t understand how to interpret this as the ability to properly estimate the effect, and I wasn’t able to understand how to do so from the authors’ description.\n\n4. The examples provided (e.g. Figure 3) often lack clarity, as both options seem plausible effects without additional context or a causal graph.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for pointing out some of the important directions. \n\n* The scope of this paper was limited to commonsense knowledge and we agree that the data used is simplistic in nature (easily to reason about by humans), and does not deal with events beyond commonsense knowledge. We also highlight the same in lines 72-75 of the paper, that CCR excludes the causal events that are beyond the reach of commonsense knowledge, for example, does “planting trees” have a direct impact on the “rainy season”? or does “providing free education” improve the “economic condition of the country/state”; does “carpooling” directly impact “air pollution”, etc. It is to be noted that the primary goal of this work is to bridge the gap between real-world causal reasoning performed by humans on a daily basis and provide the underlying causal graph for rigorous analysis as done in symbolic approaches. Performing causal reasoning on a broader scale that requires a Randomized control trial (as suggested in the weakness) is challenging for humans and is highly dependent on the methodology rather than reasoning/intelligence.\n\n* Some recent works like ROCK [https://arxiv.org/abs/2202.00436], show causal commonsense reasoning through the perspective of causal theory. Our work provides a medium to integrate the causal theory perspective (previously used by papers like Corr2Cause and Cladder for symbolic queries that are verbalized in natural language prompts to evaluate LLMs) and causal commonsense reasoning. We believe this work will be the first to consider both and will facilitate research in making LLMs more causal in reasoning.\n\n* We are sorry for the unclear explanation provided in the paper. We would like to clarify that prior arts on similar lines (ROCK[https://arxiv.org/abs/2202.00436], COLA[https://aclanthology.org/2023.acl-long.288/]) have made use of ATE estimations to perform a zero-shot evaluation using language models like RoBERTa on datasets like COPA. We followed a similar perspective to estimate ATE between the events that are further used to perform classification using the obtained Delta estimates, i.e. given two options, if the ATE of option1 is greater than ATE of option2, the causal prediction is made as option1, which is further used to compute the accuracy over the dataset.\nAlgorithm 2 uses the causal estimands $\\Delta$ to compare the causal strength between the premise event and the choice events. We consider the causal estimand computed between the premise and the available set of choices and predict the label corresponding to the high $\\Delta$ values. \nFor a given causal query from the created causal query triplet dataset $\\mathcal{D}$, where each data entry $\\mathcal{D}\\_i$ corresponds to $(p, c_1, c_2, q, l)$, i.e., premise event, choice 1, choice 2, question and the label respectively. As the task is to predict the more plausible cause/effect of the given premise event, we create two event pairs, $(p, c_1)$ and $(p, c_2)$, and compute the causal estimand $\\Delta$ for both the pairs using the temporal or the backdoor scheme (described in Algorithm 3. Note that the order of events given to $\\Delta_{\\mathcal{M}}$ is in $E_1$ and $E_2$ format, i.e. $\\Delta_{\\mathcal{M}}(E_1, E_2)$. Using the temporal precedence (highlighted as remark above), the cause event will always precede the effect event temporally. Hence, for a causal query with the question as 'cause', the causal estimand is estimated as $\\Delta_{\\mathcal{M}}(c_1^i, p^i)$, $\\Delta_{\\mathcal{M}}(c_2^i, p^i)$ and $\\Delta_{\\mathcal{M}}(p^i, c_1^i)$, $\\Delta_{\\mathcal{M}}(p^i, c_2^i)$ when the question is 'effect.' Further, based on the estimated $\\Delta_{\\mathcal{M}}$ scores, the more plausible cause/effect is predicted.\n\n* The examples provided in Figure 3 include Premise: go to store and buy cake mix. Question: Which of the following is an effect? Choice 1: come home with the ingredients. Choice 2: go to kitchen. In the given example, using counterfactual reasoning, we can say that “going to the kitchen” is possible without going to the market (if the ingredients are already available), however when going to the market, the event of “come home with the ingredients.” will always take place, making it a more plausible effect in the given choices. Similarly, example 2 in the figure is also explainable as going to market has no direct relation with heating the oven. Thank you for pointing this out, we will add a more detailed explanation for better clarity of the presented idea. \n\n\n**Response to Questions/Clarifications:**\n\n**Q1)** The primary insight as shown from the results is the LLMs lack causal commonsense reasoning abilities in general. Moreover, to provide a suitable test for the model’s understanding, we also apply the backdoor criterion and validate if it improves the performance. Interestingly, we observe that applying the backdoor criterion does help improve the performance over the simple temporal prediction. As highlighted in Table 4, we do see a significant boost in the performance when applying the backdoor adjustments. We believe this strengthens the claim of using the causal theory perspective to improve the performance of LLMs as causal reasoners. \n\n**Q2)** We completely agree that this framework can be extended to perform various other evaluations like treatment effect estimation or causal graph discovery, and incorporating these will enhance the evaluation. However, given the density of the paper, we believe these go beyond the scope of this work. We provide a few future directions on similar lines in the main paper. We thank you for pointing out some additional directions, we’ll add these to the camera-ready version of the paper. \n\n**Q3)** Please refer to the response to the last point of weakness. We hope considering the counterfactual situation will make the causal query justified. We thank you for pointing this out, we will improve the writing to explain these examples in the camera-ready version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HjpkMdEWsz", "reviewer": "Reviewer_SJyu", "summary": "This paper proposes causal reasoning in closed daily activities, which combines the causal reasoning works of open-ended causal reasoning via causal commonsense reasoning and symbolic representation-based question answering for theoretically backed-up analysis. By creating a dataset containing about 8 million queries, it can estimate the causal reasoning performance of pretrained language models. Moreover, the paper introduces theroies of causal inference (such as backdoor adjustment) to conduct in-depth analyses of causal reasoning in pretrained language model.", "questions": "1. In lines 78-80, you give a event pair, which could be erroneously treated causal-related by humans. What is the differences between this event pair and the correct cause-effect pairs in your dataset? Most of the causal relations in your dataset are about causation of necessarity.\n2. Even your dataset is in closed daily activities, the final version of the queries do not reflect this.\n3. Why is the definit setting (Cold) bettern than the plausible setting (COPA)?  I think causility is essentially a question of plausibility, there is no absolute causal relationship between two events.\n4. How would humans perform on this dataset? Can humans obtain this answer by just look into the temporal relationship between events? How many queries can be answered correctly from a statistical perspective (not from the perspective of LMs)?", "limitations": "None", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The number of constructed dataset samples is large.\n2. The usages of ATE and backdoor adjustment are novel\n3. The analyses of causal reasoning in pretrained language models are thorough and in-depth.", "weaknesses": "1. The paper is not well demonstrated, readers might be confused when reading sequentially\n2. Just for the dataset itself, there is no differences between cold and copa. Even copa may have a better quality.\n3. The dataset is limited to six scenarios, many of them are just paraphrases of the original ones.\n4. Lack of human performance as a reference.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for providing your insightful comments. Please find the response to the clarifications below.\n* Given the wider scope of this work, we agree that the paper might have become too dense, affecting the presentation quality. We would love to hear some more feedback from you to incorporate in the final version of our paper to improve the presentation quality, focussing on the reading sequence for better comprehension. \n* We would like to respectfully disagree regarding the difference between COPA and the proposed dataset. COPA (with only 1000 samples), is a more open-ended dataset and the causal queries can be of any generic domain. However, the dataset proposed in our work has enormous causal queries (8.3M in total), covering all the aspects of a particular activity (i.e. not open-ended), that essentially facilitate a more rigorous analysis (coming close to simulating the mini-turing test) and hard to be beaten by just memorization. Thanks for pointing this out, the table shown in the paper was to help consider the similarity between the datasets, we will add a more detailed comparison stating the differences between the datasets in the camera-ready version of the paper. \n* We have already highlighted in the limitation section regarding the limited set of activities, and we agree that it would be good to explore more real-world activities in the future. However, the number of causal queries that can be sampled is enormous and is not exactly the paraphrases of each other. Every scenario is different and has a different set of texts describing the events in the particular activity. The paraphrased version refers to the same action/event described by different humans with varying granularity, that are written independently by the crowdsource workers. For example, when describing an event like “preheating oven”, some people will write “preheat oven to 350 degrees”, whereas some people will write it in detail, “preheat the oven by switching on the oven and change the temperature setting to required temperature”. We agree that using the term “paraphrased” for such cases may not be appropriate, we thank you for pointing this out, and we will update the details accordingly in the paper. These different versions of the same event described enhance the dataset quality by a significant margin, providing a much more robust evaluation of LLMs. In other benchmarks, keeping only one version often limits the rigorous evaluation. \n* Please refer to our detailed comment on human evaluation in the response to Reviewer iBzV.\n\n**Response to Question/Clarifications:**\n\n**Q1)** By examples provided in lines 78-80, we wanted to highlight the importance of counterfactual reasoning required to reason causally between the events, where one does not only need to consider the temporal order of events but also needs to think counterfactually by imagining an alternate universe with the same event not occurring will cause the other event to occur or not. In the example, the event “waiting at the luggage belt” may not occur even if the person has “boarded the flight” and “not checked in the luggage”, hence boarding the airplane will not be a direct cause of “waiting at the luggage belt”. This marks the third rung of the causal ladder proposed by Pearl et. al, which is also considered a core feature of intelligence (Penn and Povinelli, 2007; Pearl and Mackenzie, 2018) [lines 23-24]. \n\n\n**Q2)** The final version of the dataset assumes the activity to happen (also considered in the evaluation prompts as context). Moreover, the marginalization of pre and post-activity events makes it a closed system for rigorous analysis where all the unobserved variables (Figure 1, represented by U, i.e. intention to perform a task) take care of separating out the causal effect from events that may occur in rest of the world. This setup also provides adherence to SUTVA which is not possible in a dataset like COPA. Some of the prior arts (ROCK[https://arxiv.org/abs/2202.00436]) have also clearly highlighted this as a major issue in natural language-based approaches. We believe this work mitigates those issues, implicitly by design, making it a much more reliable framework for claims regarding casual reasoning abilities. \n\n**Q3)** COLD provides a closed setup that can act as a suitable testbench to perform a rigorous analysis, in comparison with COPA which is an open-ended causal reasoning benchmark with only 1000 samples. Moreover, the presence of an underlying causal graph in COLD helps facilitate enormous causal queries, making use of causal dependency concepts like d-separation. Regarding capturing the causal relationship between two events, COLD provides a medium to cover all three rungs of the causal ladder, 1) association 2) intervention, and 3) counterfactuals (Pearl et. al, 2018). The relationship does not only come from plausibility but also considers the occurrence of an event causing another event. For example “checking-in luggage” will directly cause events like “collect luggage from luggage belt” to occur. \n\n**Q4)** It is to be noted that temporal precedence is generally assumed essential for defining causation, and it is one of the most important clues that is used to distinguish causal from other types of associations [Mill,\n1898, Hill, 1965, Pearl and Verma, 1995]. For our framework, we also consider the topologically sorted order obtained from the observational graphs and use the temporal order to define the causal query triplets, i.e., the cause events will always precede the effect events. We also perform a statistical perspective as suggested in the question without using any LMs. The first two rows of Table 4, show the results for the same with a detailed explanation for the statistical $\\Delta$ computation provided in the appendix. Please refer to our detailed comment on human evaluation in the response to Reviewer iBzV.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "O7AxyooezR", "reviewer": "Reviewer_LejL", "summary": "This paper proposes the COLD (Causal reasOning in cLosed Daily activities) framework, aiming to bridge the gap between open-ended causal reasoning and symbolic representation-based question answering. \n\nThe framework leverages human understanding of daily real-world activities to reason about the causal nature of events. The authors create a large set of causal queries and evaluate multiple Large Language Models (LLMs) on these queries. \n\nThe findings show that causal reasoning is challenging for LLMs, even for activities considered trivial for humans. The authors also explore the causal reasoning abilities of LLMs using the backdoor criterion. \n\nThe key contributions of this work are the development of the COLD framework, the creation of a substantial number of causal queries, and the evaluation of LLMs' performance on causal reasoning tasks. The findings highlight the need for further analysis using real-world events to properly validate LLMs' understanding of causality.", "questions": "Please refer to the weakness section for my questions.", "limitations": "The authors have dedicated a section explicitly discussing the limitations of their proposed COLD framework and offering potential solutions. The in-depth discussion contributes significantly to the paper, enhancing its overall quality.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The COLD framework effectively bridges the gap between open-ended causal reasoning and symbolic representation-based question answering, utilizing human understanding of daily activities as a solid foundation.\n- The paper addresses the crucial issue of evaluating LLMs' causal reasoning capabilities, emphasizing the significance of investigating and validating their intellectual capabilities.\n- The paper is well-written, with clear explanations, logical flow, and concise language, ensuring effective communication of key points.\n- The evaluation of multiple LLMs on a large set of causal queries reveals limitations in their causal reasoning abilities, while the exploration using the backdoor criterion provides valuable insights into causal strength between events.", "weaknesses": "Generally, I believe this paper makes good contributions. However, there are some minor issues that need to be addressed:\n\n- Since the queries are mostly automatically generated, it is necessary to support them with human annotations or expert evaluations in order to confirm the reliability of the generated queries.\n- The observational graphs are created through human annotations, which limits their capacity to cover a wide range of concepts. It would be better to discuss automated approaches for constructing such graphs in order to facilitate large-scale causal benchmarking.\n- Can the synthesized queries be used for fine-tuning? I am interested in whether splitting the observation graphs into different sets and training them on queries synthesized from the training graphs would significantly improve performance. This could greatly enhance the comprehensiveness of the paper.\n- Additionally, another set of baselines focusing on zero-shot commonsense question answering should be evaluated as well. It would be interesting to see whether transformations from commonsense knowledge bases can benefit causal reasoning tasks. I recommend checking these papers for reference.\n  - Ma, K., Ilievski, F., Francis, J., Bisk, Y., Nyberg, E., & Oltramari, A. (2021, May). Knowledge-driven data construction for zero-shot evaluation in commonsense question answering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 15, pp. 13507-13515).\n  - Wang, W., Fang, T., Ding, W., Xu, B., Liu, X., Song, Y., & Bosselut, A. (2023, December). CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 13520-13545).\n  - Kim, Y. J., Kwak, B. W., Kim, Y., Amplayo, R. K., Hwang, S. W., & Yeo, J. (2022, July). Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 2244-2257).\n- Lastly, there are some grammar typos that need to be corrected. For example, the caption of table 3 should not have \"Language\" capitalized.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your detailed and insightful review, pointing towards some suitable directions to make our work more impactful. We would like to address the raised concerns below:\n\n* We want to mention that the human/expert annotations were done to construct the underlying causal graph, capturing the causal relationship between the events. Previously, works like Jin et. al have released causal queries (https://huggingface.co/datasets/causalnlp/corr2cause, https://huggingface.co/datasets/causalnlp/CLadder) generated from the underlying causal graph that does require causal inference theory understanding to be purely annotated by humans. Whereas in our work, the commonsense reasoning nature of the proposed framework provides the primary strength for bridging the gap between current causal reasoning benchmarks/datasets in NLP. Please also refer to our detailed comment on human evaluation in the response to Reviewer iBzV, where we explain the challenges in human evaluation. \n* The advantage of using a human crowdsource dataset is the marginalization that we obtain when the same activity is written by different humans. We would like to highlight that the primary strength of this work providing real-world grounding is coming from the observational graphs that are coming directly from humans (capturing the commonsense knowledge acquired by humans). When creating a benchmark or evaluation criteria, it becomes imperative to consider human knowledge. Using the constructed graphs, the proposed design scheme helps sample enormous causal queries that essentially facilitate large-scale causal benchmarking.\n* We thank you for pointing this out. We agree that it would be interesting to explore the fine-tuning over the created samples to observe the model’s behavior. We highlight the same in the Future directions of our work in lines 354-357, “In the future, it would be interesting to sample trajectories from the observational distribution to create a training dataset and check if causal reasoning ability can be acquired by language modeling objectives (including other variants like presented in Lampinen et al. (2023)).” Some of the prior arts like Corr2Cause (Jin et. al, 2024) have considered training/finetuning over the symbolic dataset and have observed no generalization, claiming LLMs fail to robustly acquire the causal reasoning skill in out-of-distribution settings.\n* We thank you for pointing out these interesting resources. In fact, we found the strategy formulated by us for zero-shot evaluation similar to the one used in the first resource (Ma et. al), the formulation used in equation 1 of Ma et. al's work [https://doi.org/10.1609/aaai.v35i15.17593 ] is similar to our formulation, the difference being the multiple numbers of tokens being used in their setup when compared to simple option id prediction in our formulation. The remaining two resources pointed out (Wang et. al and Kim et. al) are somewhat different and consider relying on the commonsense base (Knowledge Graphs), and would require more work to evaluate in our setting of causal reasoning. We agree that another set of baselines focusing on zero-shot commonsense question answering will be a good direction to explore in the future. \n* We thank you for pointing out the typos, we will fix them in the camera-ready version of the paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank all the reviewers for their detailed reviews and suggestions. We are pleased that the reviewers found our work **novel, helping bridge the gap between open-ended causal reasoning and symbolic representation-based question answering (Reviewer iBzV, Reviewer LejL)**. We are also happy that the design of the proposed framework in the construction of the causal graph is found to be **strict and sound (Reviewer iBzV)**, laying **a solid foundation (Reviewer LejL)**. Moreover, in the analysis part, the reviewers found **the proposed backdoor adjustment and the ATE construction unique and novel (Reviewer LejL, Reviewer SJyu)** providing valuable insights into causal strength between events (Reviewer LejL) and the **supporting experiments to be detailed and thorough (Reviewer G7bs, Reviewer SJyu)**. 

Finally, on the presentation quality, we are happy that the reviewer found the paper **“well-written, with clear explanations, logical flow, and concise language, ensuring effective communication of key points” (Reviewer LejL).** There are some concerns raised regarding the high density of the paper with lots of contents (Reviewer iBzV) and the ATE experiments hard to comprehend (Reviewer G7bs) in the first iteration. We hope incorporating the suggestions made by the reviewers, will help enhance the presentation quality of our work, making it easier for readers to comprehend in the first iteration.

Meta-review: This paper presents a new framework for studying causal reasoning of daily events by bridging the gap between symbolic causal reasoning and real-world grounded commonsense causal reasoning. Reviewers recognized that this is a strict and sound contribution to create causal graphs for real-life events, and this framework supports comprehensive causal queries. Rebuttal and discussions further addressed most of reviewers' concerns, and reviewers remain generally positive. Overall, this is a rigorous step towards a correction direction for evaluating LLMs for causal reasoning. One major criticism shared by reviewers still remains: human evaluation has not been conducted during rebuttal even though it is asked for by two reviewers, thus making it difficult to assess the dataset quality and to calibrate LLM performances, so that authors need to include human evaluation, albeit small scale, in the final version.
Decision: Accept (poster)