Can Large Language Models Infer Causation from Correlation?
Authors: Zhijing Jin
Jiarui Liu
Zhiheng LYU
Spencer Poff
Mrinmaya Sachan
Rada Mihalcea
Mona T. Diab
Bernhard Schölkopf
Venue: ICLR 2024 poster (2024)
Keywords: Large Language Models
Natural Language Inference
Causal Reasoning
Correlation-to-Causation Inference
Benchmark Dataset
Causal Discovery
Primary area: causal reasoning
TL;DR: We propose Corr2Cause, the first dataset to infer causation from correlation by pure reasoning, and test 17 LLMs' performance on it.

Abstract: Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (e.g., commonsense knowledge). In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs). Specifically, we formulate a novel task Corr2Cause, which takes a set of correlational statements and determines the causal relationship between the variables. We curate a large-scale dataset of more than 200K samples, on which we evaluate seventeen existing LLMs. Through our experiments, we identify a key shortcoming of LLMs in terms of their causal inference skills, and show that these models achieve almost close to random performance on the task. This shortcoming is somewhat mitigated when we try to re-purpose LLMs for this skill via finetuning, but we find that these models still fail to generalize – they can only perform causal inference in in-distribution settings when variable names and textual expressions used in the queries are similar to those in the training set, but fail in out-of-distribution settings generated by perturbing these queries. Corr2Cause is a challenging task for LLMs, and can be helpful in guiding future research on improving LLMs’ pure reasoning skills and generalizability. Our data is at https://huggingface.co/datasets/causalnlp/corr2cause. Our code is at https://github.com/causalNLP/corr2cause.

Published as a conference paper at ICLR 2024
CAN LARGE LANGUAGE MODELS INFER CAUSATION FROM CORRELATION?
Zhijing Jin 12141 Jiarui Liu Zhiheng Lyu*
Spencer Poff
Mrinmaya Sachan² Rada Mihalcea
Mona Diab 3,5 Bernhard Schölkopf
Max Planck Institute for Intelligent Systems, Tübingen, Germany 2ETH Zürich
LTI, CMU *University of Hong Kong
'Meta AI UUniversity of Michigan jinzhi@ethz.cm ch jiaruie cmm. edu zhihenglyu. osé cww..lll..........mmmmmmmmmm.... com
ABSTRACT
Causal inference is one of the hallmarks of human intelligence. While the field Causal NLP has attracted much interest the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (c.g., commonsense knowledge). In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs). Specifically, we formulate a novel task CORR2CAUSE, which takes a set of correlational statements and determines the causal relationship between the variables. We curate a large-scale dataset of more than 200K samples, on which we evaluate seventeen existing LLMs. Through our experiments, we identify a key shortcoming of LLMs in terms of their causal inference skills, and show that these models achieve almost close random performance on the task. This shortcoming is somewhat mitigated when we try to re-purpose LLMs for this skill via finetuning, but we find that these models still fail to generalize they can only perform causal inference in in in-distribution settings when variable names and textual expressions used in the queries are similar to those in the training set, but fail cut-of-dissribution settings generated by perturbing these queries. CORR2CAUSE is a challenging task for LLMs, and can be helpful in guiding future research on improving LLMs' pure reasoning skills and generalizability)
INTRODUCTION
Causal inference, i.e., the ability to establish the correct causal relationships between variables or events, is fundamental to human intelligence. There are two distinct ways this causal inference capability can be acquired: one through empirical knowledge, e.g., we know from common sense that be
touching hot stove will get us burned; the other through pure causal reasoning, as causality formally argued and reasoned about using known procedures and rules from causal inference (Spirtes) al. 2000 Pearl 2009 Peters et al. 2017). One example is that we have the priori knowledge that the correlation between A and does not necessarily imply causality. This is a formal rule that holds true regardless of the realizations of the variables A and
With the rise of large language models (LLMs) [Radford et al., 2019 Devlin et al., 2019 Ouyang
et al. 2022, Zhang et al. 2022 OpenAl 2023 inter alia), crucial research question is whether they can do causal reasoming well. Recent studies have pointed out that LLMs are "causal parrots,"
which recite the causal knowledge in the training data (Zečević et al. 2023). Moreover, the vast majority of studies frame causal reasoning as a skill to navigate around empirical knowledge (Gordon et al. 2012 Sap et al. 2019a by Qin et al., 2019 B Bhagavatula et al. 2020). and also treat L.MMs as knowledge base when evaluating its causal skills (Kıcmman et al. 2023 Tu et al. [023] Xie et al.)
2023). However, all the above lines of research frame causality as empirical knowledge, thus relying
heavily on the quality and the coverage of the training data, overlooking the great potential of the formal causal reasoning skills to process corrlationnal information to causal conclusions.
Equual contribution. 'Equal supervision. Work originated a Meta AT internship project involving Zhijing,
Mona, and Spencer.
!Our data is at stiitter//gggiioeee.e/aaaaaaaaaa
Our code is http:////ggthtt...co///aaaaaa/aa

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Published as a conference paper at ICLR 2024
Training Corpus
sales
Hot weather
Many corretations
Vacdination
Fatality
Autiom
How can LLMs process such information? Correlation? Causation? What causes what?
company CEOs
College dropout
/
This requires the skill of inferring causation from correlation
We propose a new task: Corr2Cause Inference
Previous tasks:
Suppose know that correiates with B. Can we Infer that causes B?
No
Touching
Getting burned
Skill being tested previous work: Empirical knowledge instead pure causal inference.
A correlates with B. C correlates with However, is indepencent of Can we Infer that causes B?
Yes
"Assumpton that we expliilly mention in the samples. We suppose close system of the given variables and conseations.
Figure I: Illustration of the motivation behind our task and dataset.
Drawing inspirations from technical studies causal discovery (Spirtes et al. 2000 Spirtes & Zhang 2016 Glymour et al., 2019), we formulate a novel task for NLP, correlation-t--causatiie (CORR2CAUSE), which is important skill for LLMs. Imagine the scenario in Figure I1 where the training corpus does not tediously cover every causal relation, but more pervasively talk about correlations. such as which events tend to co-occur. Learning a good CORR2CAUSE skill can enable LLMs to draw causal relations behind the mere correlational information on the surface. For example, several decades ago, there might be an observation that female university students tend to perform better, but behind the correlational statistics the causal graph that female students have to achieve extra good performance get into universities the first place.
inference
To this end, collect the CORR2CAUSE dataset, the first dataset to the pure causal reasoning abilities of LLMs. All the questions in this dataset are centered around testing when is valid invalid infer causation from correlation. To systematically compose this dataset, ground our generalization process in the formal framework of causal discovery (Spirtes et al. 1993 2000 Glymour al. 2016 Spirtes & Zhang 2016), which provides rules about how deduce causal relations among variables given their statistical correlation in the observational data. We generate more than 200K data points, and label a correlation-causant causation statement pair as valid and only there is bijective mapping between the statistical correlation and the underlying causality.
Based on our CORR2CAUSE dataset with 200K samples, we investigate two main research questions: (1) How well do existing LLMs perform on this task? (2) Can existing LLMs be re-trained or re-purposed on this task and obtain robust causal inference skills? Through extensive experiments, we show empirically that none of the 17 existing LLMs we investigate perform well on this pure causal inference task. We also show that although LLMs can demonstrate better performance after being finetuned on the data, the causal inference skills attained by them are not robust. In summary, contributions are follows:
1. We propose the novel task of CORR2CAUSE, to probe an aspect of LLM's reasoning ability, pure causal inference;
2. We compose a dataset over 200K samples, using insights from causal discovery;
3. We evaluate the performance of 17 LLMs on our dataset, finding that all of them perform poorly, close the random baseline;
4. We further explored whether LLMs learn the skill through finetuning, and find that LLMS fail to robustly acquire this skill in -ittt--distriuttion settings. Finally, we suggest future work explore more ways enhance the pure causal inference skill in LLMss.
2 PRELIMINARIES: CAUSAL INFERENCE
2.1 DIRECTED GRAPHICAL CAUSAL MODELS (DGCMS)
A directed graphical causal model (DGCM) is commonly used representation to express the causal relations among a set of variables. Given a set of N variables X =
NN). J. we can encode the causal relations among them using directed graph G == (X, E), where E is the set of directed edges. Each edge Cj,j E E represents a causal link X; + X,, meaning that X, is a direct cause of Xy. In the context of this work, take the common assumption of directed acyclic graphs (DAGs), which most causal discovery methods use (Glymour et al. 2019), as graphs with cycles can make the causal discovery process arbitrarily hard.
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Following the graph-theorete terminology, use analogy of the ancestry tree to denote the relations between two variables. For example, we call X, as a parent of X, if there is a directed edge X; + X, in the graph, and, thus, X, is child of X,. Similarly, we denote X; as an ancestor of X; there exists a directed path from X; to x,, and, thus, X; is a descendent of X,. Note that a parent is special case of an ancestor where the directed path has length of 1.
For convenience, we also introduce the notions for some special three-variable relations. Given two variables X; and X,, we call third variable X confounder (i.e., common cause) if X3 is a parent of both X; and X,; a collider (i.e., common effect) if Xx is a child of both X; and Xy; and a mediator if Xg is both child of X,, and parent of X3-
2.2 D-PERRAAIION AND MARKOV PROPERTY
D-Separation D- D-separation (Pearl 1988) is a fundamental concept in graphical models used to determine whether two sets of nodes X and Y in a DAG g are conditionally independent given a third set of nodes Z, where the three sets are disjoint. We say that X and Y are d-separated by Z if all paths between any node in X and any node in Y are blocked by the conditioning set Z. A path between X and Y is blocked by Z if there exists a node A E Z which satisfies one of the following conditions: A is the parent node in a fork structure on the path (i.e., : + A + )); A is the mediator node in chain structure on the path (i.e.. + A + -); or in any collider structure on the path (i.e., - A - )) Z does not contain A or its descendants.
The Markov property in a DAG G states that each node X; is conditionally independent of its non-descendants given its parents, namely X; JL NonDe(X, (X,) Pa(X,), where NonDe(X; X₂) denotes the non-descendants of X; excluding itself, and Pa(X,) denotes the parents of X,, Using the Markov property, we can factorize the joint distribution of all the nodes in the graph into P(XI...., XN) = II i= (X.))) To infer the causal graph from probability distributions, a common assumption is faithfulness, namely the validity infer all the d-separation sets in the graph from the independence relations in the probability distribution. In our work, we also take this broadly taken assumption which holds for most real-world scenarios.
Markov Property
Markoy Equivalence of Graphs We denote two DAGs as Markov equivalent if they induce the same joint distribution P(X). The set of DAGs that Markov equivalent to each other is called a Markov equivalence class (MEC). Causal graphs in the same MEC can be easily identified since they have the same skeleton (i.e., undirected edges) and V-structures (i.e., structures in the form of A + B + C' where A and C' are not connected).
Obviously, there is a me-toommay mapping (i.e., surjection) between the causal graph and statistical distribution. Namely, each causal graph sufficiently determines a statistical distribution, but from statistical distribution, we cannot necessarily induce a unique causal graph. This is why say "correlation does not necessarily mean causation".
2.3 CAUSAL DISCOVERY
Causal discovery aims to learn the causal relations by analyzing statistical properties in the obser- vational data (Spirtes et al.) 1993 2000: Glymour et al., [016]] SSpirees& & Zhang 2016 Glymour (et al. 2019). can be achieved through constraint-based methods (Spirtes et al. 2000), bcore-based methods (Chickering 2002), or other methods taking advantage of the functional causal models (Shimizu et al. 2006 Hoyer et al. 2008; Zhang & Hyvärinen 2009).
To fit for the spirit of this paper to infer from correlation (expressed in natural language) to causation, we base our dataset design on the widely-used Peter-Clark (PC) algorithm (Spirtes et al., 2000). The PC algorithm is based on the principles of conditional independence and the causal Markov assumption, which allows it to efficiently identify causal relationships among variables in a given dataset. The algorithm first starts with a fully connected undirected graph among all the variables. Then it removes the edge between two variables if there is an unconditional or conditional inde- pendence relationship between them. Afterwards, it orients the directed edges whenever there is V-structure. And finally, it iteratively checks the direction of the other edges until the entire causal graph is consistent with all the statistical correlations.
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Choose the number of variables
2. Generate all unique causal graphs
Map each graph statistical correlations
Causal Graphs
Correlations
E.g., N=3
A.
Many-to-1 Mapping
A B and A C
B
1-to-1 Mapping
B. B if C, and A1 C
Hypothesize causal relation between two nodes
4. Compose the Data
Verbalize the statistical correlations
Suppose there closed system of variables, A, and C. All the statistical
Correlatione
relations among these variables are follows:
A correlates with C. B correlates with C. However, A is independent of B.
Hypothesized Causation
A directly causes B.
[The validily label equivalent the results after running the algorithm.
Validity
Valid
l.e. if the hypothesis fits all causal graphs comesponding to the set of correlations, the entailment, otherwise entailment]]
Figure 2: Pipeline of the data construction process.
DATASET CONSTRUCTION
We introduce the construction of our dataset in this section. We start with our task formulation for CORR2CAUSE, and then briefly give an overview of the data generation process, followed by detailed descriptions of each step. We conclude the section with the overall statistics of the dataset.
3.1 TASK FORMULATION
Given a of N variables x (Xi,...,
have a statement & about all the correlations among the variables, and hypothesis h describing the causal relation between the pair of variables X; and Xy. The task to learn a function f (s, h) v which maps the correlation statement and the causal relation hypothesis h to their validity v E {0, which takes the value 0 if this inference is invalid, and the value if this inference is valid.
3.2 OVERVIEW OF THE DATA GENERATION PROCESS
base the construction our dataset on several concepts of causal inference, including the DGCM, d-separation, and MECs, introduced in Section
As in the overview of our data generation process in Figure 2 we first choose the number N of variables (Step 1) and generate all the unique DGCMs with N nodes (Step 2), which will introduce in the Section 3.3 Then we collect all the d-separation sets from these graphs to identify MECs (Step 3) in Section 3.4 Then, in Step 4, create the formal form of data in Section 3.5] For each correspondence of the MEC to causal graphs, we compose the correlation statement based on the statistical relations in the MEC, and hypothesize causal relation between two variables, and produce the validity = 1 if the hypothesis is a shared property of all causal graphs in the MEC, and v = 0 if the hypothesis
necessarily true for all the MEC graphs. Finally, we introduce the verbalization
process in Section 3.6
3.3 CONSTRUCTING THE GRAPHS WITH ISOMORPHISM CHECKS
The first step of the data generation is to compose the causal graphs, as in Step 1 and 2 of Figure 2 For set of N variables each node can link to any node other than itself. To remove cycles in the graph, we make the nodes in topological order, which only allows edges X, + Xy, where j. We achieve this by limiting the adjacency matrix of the graph to only having non-zero values above the diagonal, resulting in N(N 1)/2 possible directed edges for the DAGs.
(X1,-.. Xx ). there are N(N 1) possible directed edges, since
At the first glance, for N nodes, there should be (N1-)) possible DAGs (i.e., the power set of all edges). However, there could be isomorphic graphs in this set. To avoid this, perform graph
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Nodes
Unique DAGS
Edges/DAG
#MECs
DDAG/MEC
2
out of
0.50
1.0
3
6 out of 2
1.67
5
1.2
31 of 26
3.48
20
1.55
5
302 out of 2
5.89
142
2.13
6
5,984 out of
8.77
2.207
2.71
Total
6,325
8.60
2.376
2.66
Table 1: Statistics about the source causal graphs in our dataset. Given the number of nodes, we report the number of unique DAGs, average number of edges per DAG, number of MECs, and average number of DAGs per MEC.
isomorphism check (McKay & Piperno 2014). and reduce the set so that only unique DAGs are handle large graphs, we mostly
retained, and we show their statistics in Table Although focus on smaller graphs that can still lead to a reasonably sized dataset, so we empirically set 6, but future work
open-sourced codes to extend to more nodes.
3.4 PROGRAMMATICALI GENERATING THE D-SEPARATION SETS
Based on the set of unique DAGs, we then programmatically generate the d-separation sets by graph theoretical conditions, as Step of Figure 2 To realize this step, we code an efficient graph-theoretic algorithm to check for all the chain, fork, and collider structures automatically identify the set of nodes that d-separate each pair of nodes. Using the d-separation sets and the faithfulness assumption, form the statistical correlations as follows. For each pair of nodes, they are conditionally independent given the variables in the d-separation set. If the d-separation set is empty, then the two nodes are unconditionally independent. If no d-separation can be found for the two nodes, then they are directly correlated.
Moreover, using the d-separation sets, we able cluster causal graphs to MECs. We achieve it by tracing the mapping between the causal graphs and the set of statistical correlations, and backtracking the graphs with the same d-separation sets group them in the same MEC. We show in Table I that each MEC contains on average 2.66 DAGs.
3.5
COMPOSING THE HYPOTHESES AND LABRI.
After generating the set of correlations based on the d-separation sets, we now generate the causal hypotheses. For the causal relation introduced in Section 2.1 Is-Parent, Is-Child, Is-Ancestor (excluding the parents), Is- Is-Descendant (excluding the children), Has- has-Confounder (i.e., there exists confounder, common cause, of the two nodes), and Has-Collider (i.c., there exists collider, or common effect, of the two nodes). In this way, the set of hypotheses contains all six meaningful causal relations between every pair of variables, resulting in total size of 6 N(N - 1)/2 = 3N(N - 1) hypotheses for a graph with N variables.
focus six common causal relations between two nodes
To generate the ground-truth truth validity label, we start from the correlation sets Step 3, then look up all the causal graphs in the same MEC corresponding to the given set of correlations, and check the necessity of the hypothesized causal relation. If the causal relationship proposed in the hypothesis is valid for all causal graphs within the MEC, then we generate the validity v 1; otherwise, we generate u = 0. special case of valid samples is that when the size of the MEC 1, then there is a bijective mapping between the causal graph and the d-separation sets, any hypothesis stating the causal properties of that unique causal graph is valid.
3.6 VERBALIZING INTO LANGUAGE
Finally, as the last step of Figure 2 we convert all the information above text data for our CORR2CAUSE task. For the correlation statement, we verbalize the set of correlations in Step 3 into a natural language statement s. When two variables cannot be d-separated, i.e., A A. B, then we describe them as "A correlates with B" since they are directly correlated and cannot be independent by any condition. And if two variables have a valid d-separation set C, then we describe them as "A is independent of B given C." In the special case when the d-separation set is empty, we directly say "A is independent of B." In addition, we disambiguate the setting by starting the correlation statement with the setup of a closed system of the given variables, and no hidden variables: "Suppose there a closed system of N variables, A, B.... All the statistical relations among these N variables follows:". Finally, verbalize the hypothesis, we feed the causal relation triplet (X,, X,)
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Causal Relation
Hypothesis Template
Is-Parent
(Var il directly causes (Var 51.
Is- As-Ancestor
(Var i) causes something else which causes (Var }).
Is-Child
(Var j} directly causes [Var 1).
Is- Ds-Descendant
(Var j is cause for (Var 1), but not direct
Has-Collider
There exists least one collider (i.e., common effect) of (Var i) and (Var j}.
Has-Confounder
There exists at least one confounder (i.e., common cause) of (Var i) and (Var j}.
Table 2: Templates for each causal relation in the hypothesis. We use (Var i) and (Var j} as placeholders for the two variables.
into their hypothesis templates in Table For example, we turn the triplet (A, Is-Parent, B) into "A directly causes B", as in the example Figure 2
3.7 STATISTICS OF THE RESULTING DATA
We show the statistics of our CORR2CAUSE dataset in Table Overall, our dataset contains 207,972 samples, where 18.57% of the samples have the positive label (i.e., with validity=1). The average length of the premise is 424.11 tokens, and hypothesis 10.83 tokens. We split the data into 205,734 training samples, 1,076 development and 1,162 test samples Since the main purpose of the dataset is to benchmark the performance of LLMs, we prioritize the test and development sets to have comprehensive coverage over all sizes of graphs. Specifically, we iterate through the subset of our data for each N, and split it entirely for only the test and development sets if the data is less than IK, which is the case for N = 2 and 3. For the other subsets that are larger, we randomly sample up to IK or 10% of the data, whichever is smaller, to the test and development sets. We set the cap to be IK in order to form a reasonable computation budget, since many LLMs are expensive to query in the inference mode. Aside from the test and valid sets, all the rest of the data goes into the training set.
Statistics by the Number of Nodes
Overall
N
2
N 3
N
=
4
N
=5 5
# Samples # Test
207,972
12
90
720
8,520
198,630
1,162
48
72
514
522
# Dev Train
1,076
42
72
482
474
205,734
576
7,524
197,634
Tokens/Premise
424.11
31.5
52.0
104.0
212.61
434.54
# Tokens/Hypothesis
10.83
10.83
10.83
10.83
10.83
10.83
% Positive Labels
18.57
0.00
3.33
7.50
13.01
18,85
Vocab Size
65
49
53
55
57
61
Table 3: Statistics of our CORR2CAUSE dataset, and by subsets. We report the total number of samples (# Samples); splits of the test (# Test), developement (# Dev) and training sets (# Train); number of tokens per premise (# Tokens/Premise) and hypothesis (# Tokens/Hypothesisy percentage of the positive labels (% Positive Labels), and vocabulary size by the number of unique tokens (Vocab Size). Note that the number of unique graphs and MECs are in Table
4
EXPERIMENTS
4.1
EXPERIMENTAL SETUP
We set up diverse list of LLMs for the experiments on our CORR2CAUSE dataset. To rest existing
LLMs, we first include six commonly used BERT- NLI models the transformers library (Wolf
et al. 2020): BERT (Devlin et al.) 2019). RoBERTa (Liu et al. 2019), BART (Lewis al. 2020).
DeBERTa (He et al. 2021). DistilBERT (Sanh et al. 2019), and DistilBART (Shleifer & Rush 2020).
Apart from these BERT-based NLI models, also evaluate the general-purpose autoregressive
LLMs based on GPT (Radford et al., 2019): GPT-3 Ada. Babbage, Curie, Davinci (Brown ct al. 2020); its instruction-tuned versions (Ouyang al. 2022). , text-davinci-001, text- and text-davinci-003; and GPT-3.55 (i.e., ChatGPT), and the latest GPT-4 (OpenAl 2023) by April 2023,
Note for our dataset v2.0: 0: We notice that our original data (v1.0) has duplication due to symmetric relations and verbalizations of the hypothesis. E.g., Is- Parent(A, B) has the exact hypothesis verbalization as Is- Child(B, A). Hence, for our data v2.0, we perform a careful de de-duplication, and update the data statistics in Table 3 See more version comparison details in Appendix [] Note that, due to the symmetry, the current version is random sample half of the size of the original version, the modeling results in the experiment section roughly hold.

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Published as a conference paper at ICLR 2024
Precision
Recall
Accuracy
Random Baselines
Always Majority
0.0
0.0
0.0
84.77
Random (Proportional)
13.5
12.53
14.62
71,46
Random (Uniform)
20.38
15.11
31.29
62.78
BaRe-Based Models
BERT MNLI
2.82
7.23
1.75
81,61
RoBERTa MNLI
22.79
34.73
16.96
82,50
DeBERTa MNLI
14.52
14.71
14.33
74.31
DistiBBERT MNLI
20.70
24.12
18.13
78.85
DistilBART MNLI
26.74
15.92
83.63
30.23
BART MNLI
33.38
31.59
35,38
78.50
LaMa--Bsse Models
LLaMa-7B
26.81
15,50
99.42
17.36
Alpaca-7B
27.37
15.93
97.37
21.33
GPT-Based Models
GPT-3 Ada
0.00
0.00
0.00
84.77
GPT-3 Babbage
27.45
15.96
97.95
21.15
GPT-3 Curie
26.43
15.23
100.00
15.23
GPT-3 Davinci
27.82
16.57
86.55
31.61
GPT-3 Instruct (text-davinci-O0)
17.99
11.84
37.43
48,04
GPT:3 Instruct (iext-davinci-022
21.87
13.46
58.19
36.69
GPT-3 Instruct ((ext-davinci-003)
15.72
13.4
19.01
68.97
GPT-3.5
17.79
27.78
69.46
GPT-4
29.08
20.92
47.66
64.60
Table 4: Overall performance. We report F1 (main metric), precision, recall and accuracy. For the main metric, Fl score, we use the bold font to highlight the overall best performance, and underline to highlight the best performance within each category of models.
using the OpenAl API (https://oppen) wtw.cpc.c/cpppp/mmp with temperature O. We also evaluate the recent, more efficient models, LLaMa (Touvron et al. (023) and Alpaca (Taori et al. (023).
When inspecting the behavior of finetuned models, we adopt a large set of models, including GPT- based models (GPT-3 Ada, Babbage, Curie, and Davinci) using the OpenAl finetuning API for classification at https:: httppptaffoormm opw.ai.e................. ccm/ docs gww./////ee///eeo/ooo fine- tuning open-sourced decoder-only models (GPT2, GPT2-Large, GPT2-XL, LLaMA-7B, and LLaMA2-7B), BERT-based models from scratch (BERT-Base BERT-Large, RoBERTa-Base and RoBERTA- Largge,, and BERT- Based NLI models (BERT-BaL Base MNLI, BERT-Large MNLI, RoBERTa- Base MNLI, and RoBERTa- Large MNLI) using the transformers library (Wolf et al. 2020). See training details in Appendix A
For the random baselines, we provide "always majority" to predict the majority class 100% of the time, "random (uniform)" to uniformly sample a label (i.e., 50% for each), and "random (proportional)" to sample a label from Bernouli distribution proportional to the development set label distribution.
4.2 THE CORR2CAUSE SKILL IN EXISTING LLMS
We show the performance of seventeen LLMs in Table We can that pure causal inference is very challenging task across all existing L.LMs. Among all the LLMs,, the best performance is 33.38% F1 by BART MNLI, which is higher than the latest GPTT-based model, GPT-4. Notably, many models are worse than random guess, which means that they totally fail at this pure causal inference task. The observation still holds for few-shot chain-of-thouge prompts tested in Appendix G
4.3 FINETUNED PERFORMANCE
Next, we address the question: Can we re-purpose LLMs learn this task? The experimental results Table Sal 17 models finetuned on our CORR2CAUSE seem very strong at first sight. Most models substantial increase, among which the finetuned BERT-based NLI models demonstrate the strongest performance. The best- pest-rerformin one, RoBERTa- Large MNLI, achieves 94.74% Fl score on this task, as well as very high precision, recall and accuracy scores.

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Fl
Precison
Recall
Accuracy
(Paraph.)
(Var. Ref.)
Fineluned GPT--Based Models Using OpenAI
API
GPT-3 Ada
79.85
70.47
92.11
92.92
61.73
41.57
GPT-3 Babbage
78.
69.98
88.60
92,48
62.34
43.28
GPT-3 Curie
81.23
75.00
88.60
93.77
64.93
45.32
GPT-3 Davinci
85.52
80.26
91.52
95.28
65.01
46.96
Finetuned Open-Sourced Decoder-Only Models
GPT2
89.18
88.03
90.35
96.66
56.76
31.70
GPT2-Large
94.29
92.18
96.49
98.22
55.95
31.99
GPT2-XL
94,30
91.94
96.78
98.22
60.32
43,95
LLaMA-7B
91.98
88.62
95.61
97.46
56.41
53.92
LLaMA2-7B
92.92
90.11
95.91
97.77
$2.24
49.47
Finetuned BERT-Based Models
BERT-Base
69.29
54,42
95.32
87.13
61.13
35.20
BERT-Large
85.26
77.51
94.74
95.01
63.64
38.54
RoBERTa-Base
87.60
78.47
99.12
95.73
65.58
53.12
RoBERTa-Large
89.10
82.54
96.78
96.39
65.05
60.20
Finetuned BERT-Based
NLT Models
BERT-Base MNLI
89.88
85.49
94.74
86.51
65.56
31.50
BERT-Large MNLI
90.19
84.44
96.78
96.79
67.24
52.04
RoBERTa- Base MNLI
94.27
90.35
98.54
98.17
57.42
62.83
RoBERTa-Large MNLI
94.74
92.24
97.37
98.35
55.45
67.87
(a) Performance of finetuned models the original test set.
(b) F1 scores of finetuned models the perturbed phrasing (Paraph.) and variable refactorization (Var. Ref.).
by para-
Table 5: Performance finetuned models on the original test set and perturbed test sets.
Relation Type
F1
Precision
Recall
Accuracy
FI
Precision
Recall
Accuracy
Is S--Paeent
96.18
95,45
96.92
98.67
74.80
79.31
70.77
91.73
Is-Ancestor
93.94
93.94
93.94
98.93
45.45
90.91
30.30
93.60
Is-Child
95.73
94.92
96.56
98.67
73.39
78.43
68.97
92.27
Is-Descendant
96.55
93.33
100
99.47
29.41
83.33
17.86
93.60
Has-Collider
92.19
87.41
97.52
94.64
70.70
75.00
66.90
82.04
Has Mas-Confounder
98.67
97.37
100
99.73
70.42
73.53
67.57
94.37
(a) Fine- greine performance of RoBERTa-Large causal relation
(b) Its fine-grained performance relation type after variable refactorization.
type original test
Table 6: Fine- line-grained analysis of the best best-performing model, RoBERTa-Large MNLI.
4.4 FINE-GRAINED PERFORMANCE BY CAUSAL RELATION
In addition to the overall results mentioned above, we conduct a fine-grained analysis to check the performance of the strongest finetuned model, RoBERTa-Larg. MNLI, by our causal relation types. As in Table 6a the model is very good at judging relations such as Is- Is-Parent, Is- Is-Descendant and Maa-Confouuder, all with more than 96% Fl scores. whereas it is several points weaker on the Has-Collider relations. This could be due to that the collider relation is the most special type, requiring identification of the V-structure based on both the unconditional independence based on the two variables only and correlations whenever conditioned error analysis for non-finetuned models in Appendix
common descendant. We also conduct
4.5 ROBUSTNESS ANALYSIS
Looking the very high performance of the finetuned models, we raise the next question: Did the models really robustly learn the causal inference skills?
Two Robustness Tests We design two simple robustness tests: (1) paraphrasing, and (2) variable refactorization. For (1) paraphrasing, we simply paraphrase the hypothesis by changing the text template for each causal relation to some cemantically-equivaleee variable refactorization,
alternatives in Appendix C For (2) reverse the alphabet of the variable names, namely flipping A. B. C. to Z. Y. X and so on. The inspiration behind the two robustness tests comes from the spurious correlation analysis described in Appendix
8

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Specifically, adopt the common setup of text adversarial attack (Morris al.) 2020: Jin et al. 2020) to preserve the training set and keep the same saved models, but run the inference on the perturbed test set. In this way, separate the possibilities of the models only overfitting on the training data vs. mastering the reasoning skills.
Results after Perturbation We can see from Table that all the models drop drastically, by up to 39.29 on the paraphrased test set, and up to 62.30 after variable refactorization. The best-performing model, RoBERTa-Lare Large MNLI, is especially sensitive towards paraphrasing, demonstrating the most drop among all models; however, it is the most robust against the variable refactorization, maintaining high F1 score of 67.87. We conduct fine- aee-ggraied analysis for RoBERTa-Larg MNLI under perturbation in Table We can see the the main source of the performance drop of the model comes from the two classes, Ancessst (decreasing to 45.45%) and Is-Descendant (decreasing to 29.41 %), while the other classes stay relatively robust, keeping their F1 scores over 70%.
From this analysis, we make the following suggestions future studies testing this CORR2CAUSE skill of LLMs. First, it is safe to use it as a test set to benchmark existing LLMs' performance, since the data we generate is -ito---diittitttionn from the training data of the current LLMs. Then, when testing finetuned models, it is very important to accompany adversarial attack together with the i.i.d. test set. We open- sourcs our perturbed test sets for future work to test the generalizability skill.
4.6 EXTENSION TO NATURAL STORIES
We envision our CORR2CAUSE dataset to be a foundation for future extensions to various settings, such as instantiating the variables with actual phenomena and situating the story in more natural setting. For example, the correlation does not imply causation rule can be instantiated with the ice cream sales and swimming pool attendance as the two variables, and argue that ice cream sales does not necessarily affect swimming pool attendance, because their correlation could be due to a third variable, such as hot weather. We provide case study for how to instantiate the symbolic expressions in our dataset to more natural stories, and find that LLMs such as GPT-4 can generate realistic, daily life stories that has foreseeably broad applications. See more details in Appendix B
RELATED WORK
Existing Causal Reasoning Tasks A large body of existing research of causal reasoning in NLP focuses on leveraging empirical knowledge to do tasks such as inferring the cause and effect of why an agent perform certain tasks (Sap et al. (019a). the motivation and emotional reaction in social context (Sap et al. (2019)), how people achieve a given goal with a set of concrete steps (Zhang et al. 2020), the development of a story given a different beginning (Qin et al. (019), and how in general LLMS serve knowledge base of cause and effect (Willig et al. [023] Kıciman et al.] 2023). In contrast, our CORR2CAUSE task focuses on the pure causal inference skill of models, which is a knowledge-deeendeent reasoning skill based on formally correct rules from causal inference.
Existing Logical and Inference Tasks Another related area of literature is logical and inference tasks, of which well-established one is natural language inference (NLI), to identify the semantic relationship between a pair of sentences (MacCartney & Manning 2008 Bowman et al., 2015). NLI datasets mainly focus the set and paraphrase relations. For example, "a group of boys are playing football" can entail "some guys are playing football," where "boys" are a sub-concept of "guys," and "a group of" and "some" are paraphrases. Recently, there have been increasing efforts to extend the inference task to various logical inference skills such as deductive logic and propaganda techniques (Tin al. 2022 Alhindi al.] 2022). Our CORR22 CORUSE dataset is the first dataset testing the correlation ceciiiontt-ccussee causattoo inference skill, which is unique of its type.
6 CONCLUSION
In this work, we introduced novel task, CORR2CAUSE, to infer causation from correlation, and collected large-scale dataset of over 200K samples. We evaluated an extensive list of LLMS on this new task, and showed that off-the-shelff LLMs perform poorly on this task. We also show that it is possible to re-purpose LLMs on this task by finetuning, but future work needs to be aware of the out-of-distribution generalization problem. To avoid the Goodhart's law, we recommend using this dataset to benchmark the pure causal inference skills for LLMs that have not seen this dataset. Given the limited reasoning abilities of current LLMs, and the difficulty of separating actual reasoning from training- mhaining-corpusee corpus-derived knowledge, it is imperative that our community focus on work aiming to accurately disentangle and measure both abilities. We believe the present work is first such step.

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Published as a conference paper at ICLR 2024
LIMITATIONS AND FUTURE WORK
We identify several limitations of this work and open future directions: First, in the context of this work, we limit the causal graphs to two six nodes, but future work can feel free to explore larger graphs. Another aspect is that do not assume hidden confounders in this inference problem, welcome future work to generate an even more challenging dataset to infer the existence hidden confounders, analogous the causal discovery algorithm of fast causal inference (FCI) (Spirtes et al., 2000). And also general, explorations of other causal discovery algorithms are welcomed too. Finally, lot of motivation behind proposing this task is inspired by the problem of invalid reasoning patterns in our daily reasoning (Jin et al. 2022), which could fertilize the ground for more pervasive spread of fake news. We believe false causal inference is prevalent type of fallacious beliefs, and welcome future work to connect the idea of this benchmark to more real-world false belief's based on confusing correlation with causation.
ACKNOWLEDGMENT
We thank Riley Goodside for valuable suggestions to improve our prompts to LLMs. We thank Luigi Gresele and Amir Hossein Karimi for their suggestions help us improve the formulation of our causal discovery questions.
This material is based in part upon work supported by the German Federal Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ: 01IS1 8039B; by the Machine Learning Cluster of Excellence, EXC number 2064/1 - Project number 390727645; by a National Science Foundation award (#2306372); by Swiss National Science Foundation award (#201009) and Responsible AI grant by the Haslerstiftung. Zhijing Jin is supported by PhD fellowships from the Future of Life Institute and Open Philanthropy. We also thank OpenAl for granting Zhijing quota to their API of GPT series through the Researcher Access Program.
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A IMPLEMENTATION DETAILS
When finetuning on our data, for GPT-based models, we use the default settings of the OpenAI finetuning API; and for BERT-based models, we use the trans connsformers library (Wolf et al. 2020) and train the models on server with an NVIDIA Tesla A100 GPU with 40G of memory. To for the GPU memory, we set the batch size to be 8. We use the validation set to tune the learning rate, which takes value in (2e-6, 5e-6, le-5, 2e-5, 5e-5); ); dropout rate, which takes value in (0, 0.1, 0.2, 0:3); and weight decay, which takes value in (1e-4, le-5). We train the models until convergence, which is usually around ten epochs.
Prompts When querying the autoregressive LLMs, we formulate the prompt as follows:
Question: [premise] ]
Can we deduct the following: [hypothesis]? Just answer "Yes" or "No." "
Answer:
B GENERATING NATURAL STORIES
To generate the natural stories based our symbolic expressions, we utilize the state-of-the- LLM, GPT-4, which is very good at story generation. We design detailed instructions in the prompt, and generate around 200 stories in our case study. We show two examples stories in Table 7 and the report the overall statistics in Table 8
Example (Label-Negative)
Example 2 (Label=Positive)
Symbolic Premise:
Suppose there is closed system of 2 variables, A and B. All the statistical relations among these variables are as fol-
Premise: Suppose there is closed system of 3 variables, A, B and C. All the statisti- cal relations among these 3 variables are follows: A correlates with C. B correlates with C. However, A is independent of B. Hypothesis: A directly affects C. Relation between the promise and hypoth- esis: The premise is a necessary condition for the hypothesis. So if the premise is true, the hypothesis must be true.
Form
lows: A correlates with B.
Hypothesis: A directly affects B. Relation between the promise and hypothe- sis: The premise does not a necessary con-
dition for the hypothesis.
Natural Story
Premise: Suppose there is a closed system of 2 variables, ice cream sales and swim- ming pool attendance. All the statistical relations among these variables are as fol- lows: ice cream sales correlate with swim- ming pool attendance.
Premise: Let's consider three factors: eat- ing junk food (A), obesity (C), and watch- ing television (B). There is a correlation between eating junk food and obesity, and between watching television and obesity. However, cating junk food and watching television are independent from each other. Hypothesis: Eating junk food directly af- fects obesity.
Hypothesis: Ice cream sales directly affect swimming pool attendance.
Relation between the premise and hypoth- esis: The premise does not provide nec- essary condition for the hypothesis. The correlation between ice cream sales and swimming pool attendance could be due to a third variable, such as hot weather, which increases both ice cream sales and swim- ming pool attendance. Therefore, it is not necessarily true that ice cream sales directly affect swimming pool attendance.
Relation between the premise and hypoth- esis: The premise provides the necessary conditions for the hypothesis. It establishes the independent variables A (cating junk food) and (watching television) and their correlations with obesity. Given that these are true, it supports the hypothesis that eat- ing junk food directly affects obesity.
Table 7: Examples of natural stories generated based on the symbolic form in our CORR2CAUSE dataset, showing the broad application value of our dataset as the starting point for various verbaliza- tions of the correlation-uul -otauuatttot inference task.
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Test Set Size
102
Dev Set Size
102
# Tokens/Premise
64.88
Tokens/Hypottheii
13.54
Tokens/Explanation
64.66
% Positive Labels
1.67
Table 8: Statistics of our generated natural stories. We report the number of samples in the test and de- velopment sets; number tokens per premise Tokens/Premise) hypothesis (# Tokens/Hypothesii), and explanation (# Tokens/Explanatio)) and percentage of the positive labels (% Positive Labels).
[symbolic lic For more information. the exact prompt we use is "Here is causal inference rule:
form] Please provide a real-world example instantiating this phenomenon, Format it also as "Premise:", "Hypothesis:", and "Relation between the promise and hypothesis:".
C TEMPLATES AND PARAPHRASES
We use the verbalization templates in Table 9 to compose the hypotheses for all six causal relations.
Causal Relation
Hypothesis Template
Is-Parent
(Var i directly causes Var
Is-Ancestor
(Var i causes something else which causes |ar i).
Is- Is-Child
[Var }} directly causes {Var i).
Is- D--Deseendant
[Var ]] is a cause for (Var 1). but not a direct one.
Has Has-Collider
There exists at least collider (i.c., common effect) of (Var i) and (Var }}. There exists least confounder (i.e., common cause) of (Var i; and (Var j}.
Has-Confounder
Paraphrases
Is-Paeent
IVar i directly affects (Var i1.
Is-Ancestor
[Var i) influences [Var i} through some mediator(s))
Is-Child
[Var j} directly affects (Var i).
Is D-Descendant
[Var ]} influences (Var i) through some mediator(s).
Has Has-Collider
[Var i and (Var 1) together cause some other variable(s).
Has-Confounder
Some variable(s) cause(s) both (Var i and (Var j}.
Table 9: Templates and their paraphrases for each causal relation in the hypothesis. We use (Var i) and (Var } as placeholders for the two variables.
D CHANGE LoG FOR THE DATASET VERSION UPDATE
Two Equivalent Forms
Duplication Property
De- De-Duplication Method
Is Js-Parent(1, 3)
Two exact same strings
Keep only one, by forcing i <
Is (-Chhid(),.
Anceestor(i, j) (Original)
Two different strings, but semantically equivalent
Randomly sample out of the two
Is- S-Descendent(;; j. i) (Original)
Is A--cnestor(1,, j) (Paraphrased) Descendent( (Paraphrased)
Two exact same strings
Keep only one, by forcing i <
Mas-Collider( i)
Two different strings, but semantically equivalent
Randomly sample out of the
Has-Collidert j.. i)
Has- Confounder(1,, 1)
Two different strings, but semantically equivalent
Randomly sample one out of the two
Has-Confounder(j.. i)
Table 10: De-duplication methods for the six causal relation types and their verbalizations.
De-Duplication Strategy As mentioned in Section 3.7 in the main paper, our original dataset (v1.0) has duplication due symmetric relations and verbalizations. We introduce in Table I0 several reasons for why duplicated hypotheses exist in our original data. One typical reason symmetric relations such Is Pa-rnnnt,,, B) and Is-Child(B, A), and, similarly, the paraphrased version of
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Is- As.eetott,,(A, B) and Is- S.-Descendeet(B, A). Another typical reason is the semantic equivalence in the verbalization templates, which applies to the Cas-Colldeer and Has-Confounder relations. For example, the verbalized texts of Has- Cas-ColliderrA,, B) and Collider(B, A) are "There exists at least one collider (i.e., common effect) of (A and B, and A)," respectively. which are semantically-equivaeen paraphrases of each other,
randomly keep
of the two.
Resulting Dataset Statistics after De-Duplication
Since the reason for duplication in the first place is due symmetry in the causal relation, verbalization, the resulting new data, CORR2CAUSE v2.0, is exactly a half of the original data. As we reported previously in Table 3 of Section 3.7 the total number of samples cuts down half, while the label distribution and all other properties are the same. To compose each split, we apply the same de-duplication method for the test, train, and development sets. We notice that some duplicates are across the splits, prioritize keeping the test and training sets untouched (to minimally affect the experimental results), and then reduce the development set by removing the cross-split duplicates, namely:
test ee0t_2.0= = deduplicate(test, 1.0)
train 2.0 deduplicatettrain 1.0)
dev_2.0
2.0 = dedupplicate(dev__l
(test_ train_2.0)
We expect minimal or almost no change to the experimental results. In case of the slight possibility that this change in the development set might affect the model selection the training process, future work can feel free to re-train the models and update the exact performance number.
E SPURIOUS CORRELATION ANALYSIS
The inspirations of our two robustness tests (paraphrasing and variable refactorization) come from our data analysis. We check for spurious correlations the data by reporting in Table the point-wise mutual information (PMI) between the label and any n-gram with no more than four tokens. In addition, we also report the difference of the PMI with the two labels in the [Diff] column of Table and report the top 10 n-grams.
The design spirit for our robustness test is that the models' correct judgment relies on exploiting these spurious correlations, then such reliance will be broken in our perturbations.
N-Gram
PMI w/ Non-Ent, Label
PMI w/ Ent. Label
Ditf
cause
1.692209
-1.025611
2.717820
cause for
1 1.663640
-0.983790
2.647430
A causes
1.640679
-0.951610
2.592289
A causes something
1.621820
-0.926075
2,547895
a direct
1,606052
-0.905316
2,511369
direct one
1,592673
-0.888107
2,480781
for D
1,584826
-0,878180
2.463006
for D but
1,583897
-0.877014
2.460911
1,582980
-0.875864
2.458844
for E but
1.582074
-0.874728
2.456802
Table 11: PMI between the labels and n-grams. The labels include non-entailment (Non-Ent.) and entailment (Ent.) And the n-grams include all with no more than four words. The Diff) column shows the absolute value of the difference between the PMIs with two labels. We show the top 10 n-grams with the largest differences of their PMIs with the two classes in the Diiff column.
We see that some spurious correlations rooted in the framing of the hypothesis, such "a cause (for)", and "a direct (one)" (which we use the paraphrasing task to break), and others are connected the variable names, such "for D (but)" and "for (but)" (which we use the variable refactorization to break).
F FINE-GRAINED ERROR ANALYSIS
In addition to the fine- mne-grained analysis by causal relation type in Table 6a for fine-tuned models, we also report such error analysis for non-finetuned models in Table T2
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Selected Models
Relation Type
F1
Precision
Recall
Accuracy
GPT-3.5
All
21.69
17.79
27.78
69.46
GPT-3.5
Is S--Parent
8.82
100
4.62
83.47
GPT-3.5
Is-Ancestor
0
0
90.67
GPT-3.5
Is-Child
9.84
100
5.17
85.33
GPT-3.5
Ss-Descendant
14.29
11.9
17.86
84
GPT-3.5
Has-Collider
34.24
25.51
52.07
35.12
GPT-3.5
Has-Confounder
15.33
8.86
56.76
37.8
GPT-4
All
29.08
20.92
47.66
64.6
GPT44
Is-Parent
0
0
82.67
GPT-4
Is-Ancestor
30.77
31.25
30.3
88
GPT-4
Is-Child
0
0
84.53
GPT-4
Is- S-Deecendant
26.98
17.35
60.71
75.47
GPT-4
Hlas-Collider
44.1
30.18
81.82
32.71
GPT-4
Clas-Confounder
20.67
11.53
100
23.86
RoBERTa MNLI
All
22.79
34.73
16.96
82.5
RoBERTa MNLI
Is-Parent
0
0
82.67
RoBERTa MNLI
Is-Ancestor
0
0
91.2
RoBERTa MILI
Is-Child
0
84.53
RoBERTa MNLI
Is Descendant
0
0
92.53
RoBERTa MNLI
Has CollCollddrr
43.45
39.73
47.93
59.52
RoBERTa MNLI
Has-Confee CossCooffuuder
0
0
84.45
Table 12: Fine-grained evaluation results for some selected non-fine-tuned models.
These results particularly revealing, showing how off-the-shelf models perform in recognizing specific relations. Specifically, GPT-3.5 cannot recognize ancestor relations, whereas GPT-4 fails at all direct causation recognition with parents and children. And RoBERTa MNLI only did collider relation relatively correctly. Note that, when the Fl score is zero, the accuracy number is a result of always predicting the negative class of that relation.
G LLM PERFORMANCE OPTIMIZATION
Since our experiments Section 4.2 are based on plain, zero-shot prompts, we explore whether better prompting strategies could improve the performance. We enhance the query prompt by incorporating several strategies: (1) Utilizing system prompt that specifies the model's expertise ("You are a highly duvestion-answering intelligent question answering bot with profound knowledge of causal inference."). "); (2) Including
a pair few-shot examples, one positive and one negative; (3) Implementing ohain-of-thought prompting with "Leet's i think step by step." to encourage the language model generate step-by-step reasoning. In Table [3] we present the evaluation results on the relatively affordable model, GPT-3.5, 4-point improvement in F1 over the original performance.
where the optimized prompt leads However, we can see that despite the deployment of all three strategies, the model continues to struggle with this challenging task.
Fl
Precision
Recall
Accuracy
GPT-3.5 (plain query; original)
21.69
17.79
27.78
GPT-3.55 (enhanced query)
25.44
17.29
48.11
52.01
Table 13: Performance of GPT-3.5 with different queries. We quote the original performance from Table
17


Reviews:
[{"review_id": "VkU6xhPPN8", "reviewer": "Reviewer_iTjK", "summary": "This paper presents a large-scale benchmark of causal reasoning capabilities of large language models.  This benchmark is constructed in an attempt to avoid many of the threats to the validity of previous causal LLM benchmarking approaches.  For example, it avoids reliance on numerical-data processing to focus on verbal statements; and avoids text that may have been included in the LLM's training data.  The paper uses the created benchmark set to evaluate the causal reasoning performance of a large number of open source, and proprietary LLMs, including GPT-4, and also runs experiments with fine-tuned versions of selected models.  The benchmark results indicate that LLMs do not perform causal reasoning.", "questions": "I appreciate the task the authors are addressing, and the approach they have chosen.  The major weakness of the paper I am concerned about are those relating to how the models are prompted to improve LLM behavior (lack of system prompt, few shot examples, chain of thought reasoning) and error analysis of results (e.g., perturbing inputs, running shap analysis).  That is what gives me the most pause in interpreting the results of these benchmarks---would the LLM have succeeded with different prompting strategies?  \n\nBelow, I also list a few additional questions, more about the broader framing of the corr2cause benchmark.\n\n- Some work has found differential abilities/deficiencies in LLM's symbolic reasoning vs semantic reasoning [https://arxiv.org/abs/2305.14825].  Corr2Cause seems to rely heavily on an assumption that the LLM is able to reason symbolically.  Noting the \"natural stories\" variant proposed in sec 4.6 may address this, do the authors see this as a potential concern for interpreting the results of the benchmark results in the paper?\n\n- Why is \"pure causal inference\" the right task for evaluating the causal capabilities of large language models? For example, the formal reasoning approaches describes in Section 2 that guide the creation of Corr2Cause are relatively new inventions in human history.  But I think we'd hesitate to say that people could not reason causally before, e.g., DAGs were invented.  This seems like a good opportunity to clarify what underlying functionality we are trying to achieve and how pure causal inference vs other causal reasoning tasks relate to broader goals and purposes.  And to be perfectly clear, I think this is a fine task to evaluate but would like to learn more about the authors' perspectives on relative importance, etc. of testing different facets of causal reasoning.\n\n- A deeper discussion of corr2cause in the context of reasoning and inference tasks would be interesting, especially in regards to design choices made in this benchmark as compared to others.  How were those other benchmarks designed and how does that influence the design of corr2cause?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "- The construction of the Corr2Cause benchmark is a strong attempt to disentangle \"pure causal inference\" capabilities of language models from potential construct validity threats.  For example, avoiding potentially memorized scenarios, and avoiding a requirement of numerical analysis of existing data-driven benchmarks.\n\n- The Corr2Cause benchmark is evaluated on a broad set of language models.  The additional experiments on fine-tuned models provide additional insight.", "weaknesses": "- LLM performance in many tasks is strongly sensitive to system prompt instructions framing the LLM's role and general task.  Appendix A seems to indicate that there was no such \"system prompt\" used.  The paper and its findings would be strengthened if it explored whether such instructions (such as stating the rules of causal inference or including few-shot examples) would help performance.  \n\n- Similarly, different kinds of structured reasoning (e.g., chain of thought) have been found to improve performance significantly --- and also to sometimes give insight into why failures might be occurring, e.g., by verbalizing mistaken assumptions.  The prompt in Appendix A seems to only ask for a direct Yes/No answer, however.\n\n- What do we learn from studying \"pure causal inference\" vs other facets of causal reasoning? The paper clearly has a strong perspective on what it would mean for a LLM to perform causal reasoning, naming its chosen task \"pure causal inference\".  The paper would be greatly strengthened by discussing this perspective in more depth.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "CyjFILUwms", "reviewer": "Reviewer_WuGy", "summary": "This work proposes a new task called Corr2Cause. Specifically, the input example specifies the correlation and independence of a multi-variable closed system, and the model is asked to judge the causal relationships inside this system. For this task, this work constructs a dataset containing more than 400k examples for 2-6 variable systems. 17 models are evaluated on this task, including BERT-style NLI models, and GPT-style auto-regressive LLMs. None of the models perform well out of the box. While fine-tuning BERT-style NLI models can bring significant improvements, these improvements cannot be generalized to paraphrased examples or examples with different variable names. Finally, this work also presents a case study generating another version of the dataset with natural stories using GPT4.", "questions": "1. Have you done any sub-sampling for the dataset construction? Or have you put all the possible examples and hypotheses in the dataset? This part is not clear to me.\n2. Do you have fine-grained evaluation results (like Table 6) for non-fine-tuned models? I find Table 6 to be very informative.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Differentiating causality from correlations is an important task and it is one of the clear limitations of LLMs.\n2. The description of the construction process of the dataset is detailed, well-written, and sound. The background on basic causal inference can be useful for general readers.\n3. The evaluation covers a wide range of models. Both traditional BERT-style NLI models and GPT-style LLMs are evaluated for this task.", "weaknesses": "1. I'm concerned about the difficulty of this task. This task can be very difficult for the models because it also involves multi-step reasoning, understanding symbolic systems, knowing the definition of all the terminologies, etc. Despite the clear motivation of evaluating the model's ability to do causal discovery, for me, it is unclear which part is actually the bottleneck of this task. From the current evaluation results, I am convinced that the models cannot do multi-step reasoning to figure out causality from a symbolic system, but I cannot make more fine-grained claims.\n2. It's not a major point and I don't know if the authors have enough computation resources. But while the generalization experiment design is neat, it is not surprising that BERT-style models do not generalize well to paraphrases and new variables. The evaluation would be more interesting if the authors could fine-tune some relatively small-scale GPT-style models for this experiment.\n3. In order to have good coverage for the dev and test set, this work removes all the 2-3 variable examples from the training set. Removing simple examples can make compositional tasks like the task for this work very hard. I wonder if this design makes this dataset unnecessarily difficult, and whether such a dataset split is the best configuration for this task.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "zwxSAECxlB", "reviewer": "Reviewer_fGug", "summary": "To investigate the capability of llm on inferring causality from correlations in text data, this paper proposed a novel corr2cause benchmark dataset. Apart from introducing the detailed dataset construction process, this paper also conducted extensive experiments over 17 llms to verify the effectiveness of the dataset. Meanwhile, this paper also verified whether llm can learn the causality skill after finetuning. In conclusion, this paper made an interesting attempt over casual inference ability of llms.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThis paper design a novel corr2cause dataset to verify the causal inference ability of LLMs.\n2.\tThis paper provided a detailed dataset construction introduce, which makes the dataset very reasonable.\n3.\tThis paper conducted extensive experiments over 17 LLMs. Moreover, further experiments were also conducted to verify whether LLMs can learn causal inference ability by finetuning. The experiments are very convincing.\n4.\tThis paper made an early and interesting attempt over the causal inference ability of LLMs, which can inspire plenty of related work.", "weaknesses": "1.\tFor the dataset construction process, the authors leverage variables to represent the input data. However, what do variables represent? Entity in sentence? sentences or what? The authors should provide more details information. \n2.\tFor the causality discovery, the authors only provided the results of PC algorithm. However, the precision of PC algorithm is unclear. Whether the results are reliable is still unclear. The author should provide more details about how to ensure the confidence of the results generated by PC algorithm.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (poster)