LLMs Struggle to Balance Reasoning and World Knowledge in Causal Narrative Understanding
Authors: Khurram Yamin
Shantanu Gupta
Gaurav Rohit Ghosal
Zachary Chase Lipton
Bryan Wilder
Venue: ICLR 2026 Poster (2026)
Keywords: Causal Inference
Large Language Models
Reasoning
Narratives
Primary area: foundation or frontier models, including LLMs
TL;DR: In this paper, we examine the failure Modes of LLMs for causal reasoning on narratives and the unreliable shortcuts LLMs take to make causal inferences.

Abstract: The ability to robustly identify causal relationships is essential for autonomous decision-making and adaptation to novel scenarios. However, accurately inferring causal structure requires integrating both world knowledge and abstract logical reasoning. In this work, we investigate the interaction between these two capabilities through the representative task of causal reasoning over narratives. Through controlled synthetic, semi-synthetic and real-world experiments, we find that state-of-the-art large language models (LLMs) often rely on superficial heuristics—for example, inferring causality from event order or recalling memorized world knowledge without attending to context. Furthermore, we show that simple reformulations of the task can elicit more robust reasoning behavior. Our evaluation spans a range of causal structures, from linear chains to complex graphs involving colliders and forks. These findings uncover systematic patterns in how LLMs perform causal reasoning and lay the groundwork for developing methods that better align LLM behavior with principled causal inference.

LLMS STRUGGLE TO BALANCE REASONING AND WORLD KNOWLEDGE IN CAUSAL NARRATIVE UNDER- STANDING
Khurram Yamin
Shantanu Gupta
Syamin@andresn cmu cww.ccu.......oo edu
shantang@ cmu wmt.cmu...co edd Carnegie Mellon
Camegic Mellon
Gaurav Ghosal
Zachary Lipton z1 zlipton@ancee cmu edd Carnegie Mellon
gghosal @andrew. cmu, edu Carnegie Mellon
Bryan Wilder
bwi leeeraeaaeeeeeeee rew. cmu. edu
Carnegie Mellon
ABSTRACT
The ability to robustly identify causal relationships essential for autonomous decision-making and adaptation to novel scenarios. However, accurately inferring causal structure requires integrating both world knowledge and abstract logical reasoning. In this work, we investigate the interaction between these two capabili- ties through the representative task of causal reasoning over narratives. Through controlled synthetic, semi-synthetic and real-world experiments, find that state- of-the-art large language models (LLMs) often rely on superficial heuristics for example, inferring causality from edge without attending to context. Furthermore, we show that simple reformula- tions the task can elicit more robust reasoning behavior. Our evaluation spans a range of causal structures, from linear chains to complex graphs involving colliders and forks. These findings uncover systematic patterns how L.LMs perform causal reasoning and lay the groundwork for developing methods that better align L.L. behavior with principled causal inference.
order recalling memorized world knowl-
INTRODUCTION
Many successful applications of LLMs to causality leverage the ability of L.LMs to absorb and summarize large amounts of world knowledge from large-scale unsupervised data. However, more ambitious roles for LLMs could require stepping beyond knowledge from pretraining and moving towards reasoning about causal structure in context, merely recalling associations. In this work, we explore whether such contextual causal reasoning capabilities arises naturally from large-scale pretraining.
Robust causal reasoning is particularly challenging as it relies on combination of knowledge and reasoning capabilities. On the one hand, causal reasoning relies on deductive or mathematical skills to correctly apply axioms (e.g., Pearl's do-calculus), making inferences from graphical structure describing cause-effect relationships. However, unlike mathematical reasoning benchmarks (Cobbe) al.] 2021 which draw from relatively constrained set of problem solving strategies and results arriving at correct causal inferences often requires leveraging domain-speciff knowledge about the events variables involved instantiate such graph. These two abilities must be balanced; models must go beyond blindly retrieving memorized associations and knowledge to identify the correct relationships under atypical counter-intuitie settings.

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Prior works have primarily studied reasoning and world-knowledge of LLMs separately. For example, benchmarks on mathematical reasoning or coding typically study these capabilities in isolation - with minimal external world knowledge needed to solve problems. On the other hand, benchmarks for knowledge intensive tasks can generally be solved by simple retrieval of memorized knowledge. Thus, the interplay of knowledge retrieval and reasoning (and potential conflicts between them) remains understudied.
In this work, we study the interplay between reasoning and using the right amount of world knowledge through causal reasoning over textual narratives. In order to characterize LLMs' capabilities across the full range of interactions between these two characteristics, we need the ability to separately vary the difficulty of an instance along both dimensions. Accordingly, we construct a new set of tasks based on textual narratives generated from synthetic, semi-synthetic, and real-world causal relationships. Each instance starts from a true causal graph structure on a set of nodes Vg... from which we generate a narrative consistent with the true graph. Then, we present the L.M with only the narrative and ask it: (I) determine whether Vi causes V, (directly or indirectly) for some pair i, j; and (2) given node identities (V1,..., Vx), reconstruct a causal graph faithful to the narrative.
We then systematically control task difficulty along two axes. Along the world knowledge conflict axis, we manipulate how much the narrative diverges from memorized or Common--sense causal knowledge (e.g., applications to atypical settings not commonly seen in pre-trainingl This tests the LLMs ability to reason using the actual context of the story as opposed to its memorized knowledge. Along the graph reasoning complexity axis, we vary the number of nodes in the underlying causal graph and the structure of the graph itself (e.g. simple chains versus graphs with complex structures including both forks and colliders). This tests the LLMs ability to extend its reasoning beyond simple scenarios to more complex situations.
Together, these design choices allow us to characterize LLMs' performance across the full spectrum of both dimensions of task difficulty. We find that gaps in performance across this spectrum are well-described by two distinctive failure modes related interference between reasoning and world knowledge in causal inference. Firstly, we show that LLMs are influenced heavily by prior that causes are likely to appear before effects in a narrative. We observe that when the narrative is constructed in the reverse topological order of the causal chain (i.e., the edge V/2 Vi+1 is narrated before Vi-1= - Vi), the performance of the LLM suffers as it often assigns the cause to an earlier event and the effect to later event in the narrative. Secondly, we show that LLMs use their parametric causal knowledge (i.e., if an event typically causes another event) as a shortcut to answer causal questions. Thus, when the cause-and-effect pairs implied by the narrative conflict with the parametric knowledge, the I.LM often ignores the specifics of the narrative and defaults to its parametric knowledge. Neither prompting with Chain of Thought (CoT) (Wei et al. 2022) nor In-Context Learning alleviates these failures.
However, LLMs are much less impacted by variation in the reasoning difficulty of the task when the prompting scheme explicitly isolates reasoning and world knowledge. First, we find that asking the LLM to extract the entire causal graph implied by the narrative results in a high degree of success at correctly ordering individual events, largely avoiding both failure modes described above. However, these benefits dissipate if the model is prompted to use the extracted graph alongside the narrative. Second, I.MMs exhibit only slight performance degradation when reasoning over narratives that display more complex graph structures than chains, for example forks or colliders. Third, while LLMs often struggle with longer narratives containing more events, this failure is also substantially mitigated by asking the LLM to just extract a graph. All together, our results paint a more nuanced picture of LLMs' causal capabilities than simple success or failure and suggest that future development should focus on isolating and then composing LLMs' strengths at reasoning and world knowledge in order avoid conflicts between them.
2 RELATED WORKS
Causal Reasoning in Large Language Models Jin et al. (2023) develop benchmark for testing causal reasoning in LLMs given causal graphs, finding that language models can struggle with the task. However, the queries examined in Jin et al. (2023) require probability calculations, potentially conflating causal reasoning and arithmetic failures. Tan et al. (022) shows the capability of a neural network trained news data to label causal structures in individual sentences. Joshi al. (2024b)
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chronicles failure modes in textual, but non-narrative form data (e.g. text formulaically written as Event 1 Causes Event 2 Causes Event 3 Causes Event 4). Our paper expands upon such a line of work by testing the LLM's 's abilities in both real and synthetic texts that much more closely resemble those seen in everyday life. Another contrasting work, Jin et al. 2024), uses only statistical language indicating event correlations as input.
(Gordon et al. 2012 Joshi et al., 2024a Ho et al., 2023 Zhang et al. 2023 Wang et al. 2023
Ashwwni al. 2024) study causal reasoning ability as it relates to inferring causal relations based on "common sense". In such common-sense based settings, it is straightforward for models to simply rely on memorized knowledge from pretraining and achieve good performance, without leveraging any more general causal reasoning capabilities. Our work seeks to disentangle this general causal reasoning ability by specifically testing cases where causal relationships may contradict common- sense knowledge. This serves as a more robust measurement of the causal reasoning capabilities in unfamiliar and atypical scenarios. Empirically, we show that models struggle significantly adapting to unfamiliar causal relations.
Another important distinction of our work is the focus on longer-form narratives. Existing works such as (Gordon et al. 2012, Zečević al.) [023 Ho et al. 2023; Frohberg & Binder 2022 Li et al. 2023 Gao et al.) 2023) primarily examine short-form questions about a single causal relationship. On the other hand, our work examines longer and more complex sequences of events. Moreover, in contrast to domain-specific question banks such as Intuitive Physics studied in (Zečević et al. 2023), our narratives examine more diverse range of topics (as illustrated by the sample narratives presented), As result, our dataset provides a more realistic and diverse examination of LLM causal reasoning capabilities than prior works. As such, our work is unique in that we are the first paper to analyze non-common sense based causal reasoning in narratives that use everyday language.
Causal Story Generation Kıcıman et al. 2024) shows that LLMs have strong abilities to generate causal texts. Ammanaabrolu et al. (2020) introduces soft causal relations causal constraints that match what readers expect and uses commonsense inferences to bridge high-level plot points, resulting more coherent narratives that align with everyday causal expectations. (Tian et al. (202T) contributes by employing counterfactual knowledge to generate hyperboles, making story generation more realistic. [Li et al. (2022) shows that asking a model to explain cause or effect by generating new text conflates language generation with prediction; instead, their approach asks the model to simply indicate the sentence number representing the cause or effect, leading to stories that better respect causal relations. For our synthetic text generation, we focus on creating narratives that extremely explicit and simple. In contrast to Ammanabrolu et al.] (2020) and [i et al. (2022)* s approach of bridging events using commonsense, our narrative scheme already embeds explicit causal language between events that are causally related so that no inference or common-sente required from the reader to reason about causality. Furthermore, our experiments often intentionally contradict common-sent sense parametric knowledge to check the model's 's ability to solely rely on the self-contained narrative. Similarly, regarding Tian al. (202I) we opted to avoid abstract language structures that might confuse even human readers.
reasoning is
EXPERIMENTS WITH SYNTHETIC DATA
3.1
SETTING
Synthetic Narrative Generation In our synthetic experiments, we use three leading LLMs: Ope- nAI's GPT-4o (OpenAl et al., 2024). Anthropic's Claude 3.5 Sonnet (Anthropic 2024), and the open source LLama 3.1 8b (Grttafirri al., 2024). While we focus on GPT-4o the main text, results from other models are in the Appendix. The purpose of our synthetic setup is to carefully control the conditions under which the L.LMs are tested. terms of the general setup of our fully synthetic experiments, we first use the LLM to generate events (which are real world phenomena like rain or plants growing). Then these events are linked together into a chain graph G that acts as the causal ground truth (eg rain + plants growing). The LLM is given G and asked to create narrative that stays faithful to the causal relationships in G. These narratives are checked by researchers to ensure consistency with their base causal graphs. More specifically, when constructing the dataset, we asked researchers (3 non- non-author graduate students who were blind to the true underlying graph) to reconstruct the causal chains given just the narratives, and over 98 percent of the time (out of 150
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Accuracy
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12 Chain Size
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Figure I: GPT-4o Test of the LLM's ability to reason narratives written in the Forward and Reverse topological orientations. Chain size is the number of nodes in ground truth G. The "Graph" prompting method uses only the extracted graph G' to reason, "Narr-Graph" uses both the narrative and extracted graph, and "Standard, CoT, In-Context" all use only the narrative. Accuracy measures LLM answer agreement with G' (we test every possible ordered pair of variables and check whether the extracted graph correctly implies the existence and direction of the corresponding causal edge when compared to the ground truth G), and consistency measures agreement with G'. The points in the graph are represented with slight horizontal stagger around the relevant chain sizes (4,8, 12 etc) for ease of visual understanding. We show 95% CI.
random samples), the humans were able to find the unique correct causal ordering (Appendix F). Roughly 2500 narrative samples were generated. To ensure a variety of events go into the narratives, generate 100 to 1000 distinct events at a time and randomly pick the small number needed for narrative construction (all narratives in supplementary files and select narratives in Appendix).
Providing only the narrative input (and not G), we then ask the LLM to find G'. the predicted underlying causal structure expressed by the narrative. In other words, the LLM asked to output causal graph that thinks embodies the relationships in the narrative. Next, series of causal questions is created by randomly sampling 10 tuples of events from G and asking the LLM whether event in the tuple causes the other based on the narrative and/or G'. All results taken and aggregated over random seeds, with the CI being taken after aggregation.
Prompting Strategies We evaluate five prompting styles for causal reasoning where the names in italics represent those used in the legends of figures: Standard QA Prompting (Standard), where model simply asked to identify the causal relation between two narrative events; Chain-of- Thought (CoT), which instructs the model to articulate step-by-step reasoning before answering; In-Context Learning (In-Context), which precedes the query with illustrative question-ane answer examples; Explicit Causal Graph Extraction (Graph), which asks the model to generate causal graph G' over all events and assesses whether the ordering of the target pair is correct; Garrative-Augmenter Graph Extraction (Narr-Graph), which first elicits G' and then supplies both G' and the original narrative for joint reasoning about the causal pair. Exact prompts are in Appendix A
entire
3.2 IMPACT OF EVENT ORDERING
Our experiments show that L.MSs rely on the ordering in which the events are verbalized in a narrative when determining causal relationships. To investigate this, we started with randomly generated events that were used to make a ground truth graph G. During the creation of the narrative, we specified that the LLM either places the events in (1) the order that matches the topological causal ordering of the graph (e.g., if event A (indirectly or directly) causes B, then event is mentioned before B in the narrative,, or (2) way that runs opposite the causal ordering (event B would be mentioned before event A in the narrative even though A (directly or indirectly) causes B). We refer to these the Forward and Reverse topological ordering, respectively. As example, the following is
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vs Accuracy
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Prompiing Method
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Figure 2: (Left) GPT-4o test of the LLM's ability to reason narratives that agree with parametric knowledge (Causal) and disagree with parametric knowledge Anti-Causall). (Right) GPT-4o test of the LLM's ability reason narratives generated from Complex graphs opposed to Simple chain graphs. Label descriptions for both images match those of Figure and 95 CI is shown.
GPT-4o generated Reverse topological narrative for the causal chain: Art exhibition Wine tasting Charity fundraiser:
The charity fundraiser was made possible because of the successful wine tasting event that attracted numerous generous patrons. The wine tasting was organized as result of the art exhibition drawing in sophisticated audience interested in cultural experiences.
Each edge in the narrative is verbalized in the opposite order to its place in the causal chain. All narratives can found the linked code.
LLMs Rely on Event Ordering Across Prompting Strategies As shown in Figure T (left), in the Forward direction, standard QA, CoT, and In-Context prompts all perform very well. This is in contrast to the Reverse orientation when we look at the performance of the standard QA, COT, and In-Context prompts. From this plot, we can that naive COT and In-Context prompting do not seem to significantly boost accuracy under our conditions. Perhaps more interestingly, we find that the way the LLM answers questions using the narratives graph G" that the LLM builds when asked to predict the underlying graph structure (see consistency plot in right side of Figure] [ where consistency measures agreement between the answers of the LLM and G' whereby we see what percent of answer implied by G' are also implied by the LLM). In the Reverse orientation, answers given by the extracted causal graph G' and the previously discussed prompting strategies seem to differ greatly. Additionally, the trend of those prompting strategies on the consistency plot for the Forward orientation narratives (comparing performance to G') mirrors their trend on the accuracy plot which compares performance ground truth G (left side).
always consistent with the causal
Explicit Causal Graph Extraction Avoids Shorteuts This led us the accuracy of only using the extracted graph G" to answer causal questions (Figure "Graph" Method), In this case, once G' extracted by the LLM, it is not given the LLM again to answer questions (but rather used directly with graph traversal). We found that this strategy did significantly better in the Reverse direction than the other prompting strategies (~ 50 % better). Surprisingly, using G' in the Reverse direction narratives to answer causal questions did as well as using G' in the Forward direction narratives. Next, we tried prompting using the narrative and (the LLM is given This technique could be thought of type of CoT prompting strategy. However, in the Reverse direction narratives, the increase in accuracy achieved by only using completely dissipates. We conjecture that the process of building the extracted Causal Graph G' forces the LLM to engage in long term reasoning instead of using the simple shortcut, but when the narrative is again provided the L.M defaults back to the shortcut.
this case in the prompt).
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3.3 IMPACT OF PARAMETRIC KNOWLEDGE (IN)CONSISTENCY
Experimental Setup We also find that LLMs tend to rely on parametric knowledge when it is present, and can fail when narratives are inconsistent with the LLM's parametric knowledge. To test this, we elicit the LLM's pre-existing parametric knowledge when generating the event chains. We prompt the LLM to pick series of events such that each event has some relation to the subsequent event - either the event is Causal to the next event (c.g.. disease causes shorter lives) or the event Anti-CCusal (e.g., disease causes longer lives). For example, we might know that node Causal to node 2 from parametric knowledge. Thus, when we make the causal ground truth graph 1 + 2 (this disagrees with parametric knowledge), create a narrative from it, and then ask the LLM if node 1 causes 2 based on the narrative: it should say yes based on the narrative even though that disagrees with its parametric knowledge. After the ground truth graph is created, we generate the narrative in the Forward topological orientation to avoid confounding failure modes. The full process (along with illustration) explaining how the parametric and causal graphs are created is in Appendix A.2 As textual example, assume that know parametric anti-causal link exists from stressful job to increased happiness, and from lack of sleep to improved cognitive function. We can then construct the causal chain Stressful Job + Lack of Sleep + Increased Happiness - Improved Cognitive Function. From this causal chain, we create the narrative:
Anti-
The constant demands of stressful job led her experiencing chronic lack of sleep. Surprisingly. she found that the lack of sleep heightened her sense of euphoria, making her unusually cheerful at work. Increased happiness from this unexpected cheerfulness seemed to improve her cognitive function.
If the LLM is asked if a stressful job leads to increased happiness, the parametric knowledge shortcut indicates the answer should be no however, the shortcut fails as the narrative indicates that a (indirect) causal link does exist.
Models Exploit Parametric Knowledge We find that, in synthetic experiments, the LLM finds the correct causal relation generally only when that relation agrees with its parametric knowledge. This exemplified the plot Figure (left) where we see good performance on narratives that agree with parametric knowledge (Causal parametrie knowledge) and poor performance narratives that disagree with parametric knowledge (Anti-Causee parametric knowledge). We also notice interesting phenomenon for the Anti-Cause parametric case where using just the extracted graph provides massive improvements over any prompting strategy that involves using the narrative to directly answer questions. This strategy is comparable in performance to when the parametric knowledge is Causal. It seems that the narrative may only serve to distract the LLM when parametric knowledge disagrees with the narrative.
3.4 IMPACT OF NARRATIVE COMPLEXITY
In the previous sections, we identified two shortcuts which models exploit in causal reasoning tasks. Here, we test the influence of narrative complexity on these failure modes. We examine two measures of complexity; (a) the narrative length and (b) the presence of complex graph structures.
Narrative Length In conditions where the LLM exhibits failure modes (Reverse and Anti-Causal orientations), the performance also tends to decay as the size of the narrative and the number of events in the narrative increases. As we can see in Figures (Left,,, it seems that the longer the narrative is, the more the LLM relies on shortcuts instead of performing reasoning. However, the extracted graph G" can often maintain a consistently high level of accuracy across narrative sizes even for cases when a failure mode would normally be exhibited.
Causal Graph Complexity As the bulk of our work has focused on detecting the simplest failure modes possible, we studied narratives with an underlying chain graph structure. However, the presence of more complex causal structures in the narrative could exacerbate the existing failure modes or trigger novel failures. To study this, we create causal graphs utilizing two common causal structures: Forks (one node has causal relationship to multiple other nodes) and Colliders (multiple nodes have a causal relationship to the same node). We generate narratives (the complete algorithm is described in Appendix A.3) such that each underlying causal graph contains at least one of these

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structures, and may randomly contain multiple such structures based the size of the narrative. An example is shown in Figure
Heavy Rainfall
Narrative:
The heavy rainfall not only caused a power outage in several neighborhoods but also led to flooded sireety. The aftermath of the power outage (disabling traffic lights) and the flooded roads (blocking street ac- cess) caused traffic jam.
Power Outage
Flooded Streets
Traffic Jam
Figure 3: Causal graph with story showing a fork (first sentence) and collider (second sentence).
As can be seen in Figure (right side), we find that while the LLM generally performs worse at reasoning about the complex narratives than simple narratives (with underlying chain graphs), the gap is very starkly less than can be seen in the other failure modes. This finding can be supported by (Dettki et al. (025) which finds that GPT-44 reasons similarly humans on single sentence that describes one collider relation. Our work extends their work by using a long-form narrative based on causal graph with potentially multiple colliders and forks instead of only one collider.
4 EXPERIMENTS WITH REAL WORLD CAUSAL GRAPHS
In this section, we extend our analysis to narratives involving real-worle causal graphs from CauseNet (Heindorf al., 2020). a large-scall knowledge graph of (claimed) causal relationships between real-world concepts. We perform experiments using the GPT-4o (OpenAl et al. (024) and Llama-3.1 8B models for our experiments. We concentrate our analysis on the same factors (positional biases and parametric knowledge consistency) explored in the semi-synthetic settings.
The CauseNet dataset can represented as collection of tuples {(C,, F,, S,) where C, denotes the cause (e.g.. fatigue), E; denotes the effect (e.g., accidents), and S; is set of sentences (extracted from Wikipedia and Clue (Callan 2012)) that entail causal relationship from C; + Vi of various lengths, where each causal
to E, We retrieve causal chain graphs V12 - V22 relation V2 + V is from CauseNet and verbalize these chains as narratives in the following ways:
Semi-synthetic narratives. In this setting, cally verbalize them via the LLM. In particular, we prompt the LLM to generate sentences for each edge (V; Vi+1) in the causal graph, while ensuring the sensibility of the entire narrative. For eammple, the following is a narrative for the chain fatigue - accidents > injury:
real causal graphs from CauseNer but syntheti-
Fatigue can cloud judgment and slow reaction times, leading to an increase in accidents on the road. As result, these accidents often lead serious injury for those involved, highlighting the dangerous consequences of driving while fatigued.
Real-world narratives. For the real-world narratives, the sentence for each edge is chosen from the CauseNer dataset. To ensure that the narrative as a whole remains coherent, we prompt the LLM to ensure that the sentences for every pair of adjacent edges logically follow each other. For example, the following is the narrative for the causal chain fatigue accidents injury:
Workers work long hours in mines and factories where fatigue and lack of concentration can casily cause accidents. These accidents are the leading cause of injury in this country for people ages 1-34.
Additional examples of semi-synthetic and weal-world narratives are presented in Appendix C.I (the entire set of narratives used for our experiments is available in the linked code).
Prompting Strategies For simplicity, we limit the prompting techniques used to (see Appendix for the prompt templates): Standard QA Prompting, Chain-of-Thouuee and Explicit Causal Graph Extraction. We evaluate the accuracy for each pair of nodes (V,, V2) for the three prompting strategies on the semi-synthetic and real-world narratives.
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Figure 4: The accuracy of various prompting strategies (error bars denote 95% Cls). We observe that the accuracy is lower in the reverse direction (and tends to decay as the chains get longer).
4.1 IMPACT OF EVENT ORDERING AND CHAIN LENGTH
As described the previous section, we verbalize each causal chain graph V/1 - V22 - VN from CauseNer into narrative in the forward and reverse topological order. In both the semi-synthetic (Fig. leftt) and real-world narratives (Fig. right), the Forward Graph strategy performs the best, with its accuracy remaining stable even the chain length increases. We observe that Forward Standard and CoT outperforms Reverse Standard and CoT, with the Reverse accuracy declining substantially as the chain size gets large. We also see that in this regime, extracting the causal graph makes inference in the Reverse orientation competitive with inference in Forward.
4.2 EFFECT OF PARAMETRIC KNOWLEDGE CONSISTENCY
Experiment Setup Next, we analyze the extent to which the LLM relies on its parametric knowl- edge answer causal reasoning queries as opposed to the causal structure expressed in the narrative. For every pair of nodes (Vi, Vý) in the chain graphs, by asking the LLM whether a causal effect between the two nodes would be atypical (see Appendix identify cause and effect chains
elicit the paramctric knowledge of the LLM
B.2 for the exact prompts utilized). Through these prompts, which contradict the model's parametric knowledge. For example, in a chain graph from our dataset, there is path from streambank erosion to higher prices, but this contradicts the LLM's parametric knowledge since this causal effect may not typically exist in the real-world. In total, we find that roughly percent of the relations in CauseNet violate the LLM's pretraining knowledge. We sampled narratives from CauseNet until we got 100 (of chain sizes between 3 and 9) narratives that contain relations that violate the LLM's pre-training knowledge and 100 that are consistent. These narratives are constructed in the Forward topological ordering to avoid confounding failure modes.
LLM Performance Suffers on Atypical Causal Relations We evaluate the three prompting
strategies separately on the subsets of cause-and-effect pairs that are in agreement and in conflict with the parametric knowledge (see T.. We observe that when there is no conflict (i.e., the parametric knowledge agrees with the causality expressed in the narrative), the accuracies with and without CoT are greater than 90%. However, when the parametric knowledge conflicts with the narrative's 's causality, the accuracy is significantly lower, about cause and effect a narrative, the L.LM seems to rely heavily on its parametric knowledge and unable to grasp the specific causal chains expressed the narrative itself (despite the causal chains as whole being realistic).
with CoT. This suggests that when asked to reason
Explicit Causal Graph Extraction Avoids Shorteuts Interestingly, when using extracted graph for performing causal reasoning, the performance is very high, both with and without conflicts. This is likely because when asked to extract the graph from the narrative, the LLM pays more attention to the entire narrative opposed to when directly queried
cause-ame cause-and-efie and-effect pair (where the LLM defaults to its parametric knowledge). These results show that even when the LLM constructs a reasonably good causal chain graph, the I.LM does not leverage this graph when queried directly
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CoT
Graph
Semi-synthetic
Without Conflict
99.8
99.6
99.9
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67.2
73.1
98.7
Real-world
Without Conflict
90.9
89.2
97.9
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52.1
57.6
93.2
Table 1: The average accuracy across different narratives with the three prompting strategies parti- tioned by whether the cause-effect pairs conflict with the LLM's parametric knowledge (we omit the 95% CIs they are smaller than 0.3).
SemiSyrihwiic Simple Cumples
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Figure 5: GPT-40 accuracy on narratives generated from Complex graphs as opposed to Simple chain graphs for semi-synthete narratives (left) and real-world narratives (right). 95 % CI is shown.
about the causal effects the narrative (even with CoT), further highlighting the advantage of extracting the causal graph directly.
4.3 NARRATIVE COMPLEXITY
We can see from Figure 4 that LLM performance degrades with narrative length, especially when failure mode is present. We furthermore experimented with complex narratives with causal graphs containing forks and colliders (full graph and narrative creation algorithm in Appendix B.3). We can see in Figure that in both the semi-synthetic and real-world settings that complex narratives (with colliders and forks) perform worse than simple narratives that have a causal chain graph as the ground truth. This gap while clear and noticeable, 't as stark failure from parametric knowledge conflict (Table D or topological ordering (Figure 4 We do furthermore note that this is one area where extracting an explicit causal graph does not seem to significantly improve performance.
DISCUSSION
Our work takes initial strides towards examining the success and failure of LLMs reason causally on narratives that express causal events. We focus on two questions of key importance in causality: (1) Does one event cause another? (2) Can the LLM extract the causal graph from the narrative. We find three significant failure modes of LLM reasoning by conducting experiments in carefully controlled synthetic, semi-synthetic and real-world settings: Firstly, we find that L.MMs rely heavily on topological ordering. performing well when the ordering of events in the narratives matches that of the ordering of the underlying causal graph. Secondly, we find that LLMs rely on their parametric knowledge shortcut to infer causal relations. Finally, we examine the role of causal structure complexity, finding that LLM accuracy degrades as the narrative length increases. Furthermore, LLMs perform slightly worse reasoning when narratives contain structures such colliders and forks. Beyond these failure modes, we show that more reliable causal reasoning can be elicited by prompting the LLM explicitly identify the causal graph. One limitation of our work is that

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there are other forms of causal reasoning that we did not test for in the narratives. This motivates many potential directions for future work. For example, it could be interesting to ask the LLM to reason about counterfactual cases. Our analysis also has implications for algorithmic interventions to improve causal reasoning. The failure modes we identify in this paper could inform the design of targeted synthetic tasks use in finetuning for improved causal reasoning. Additionally, our findings on the benefits of extracting causal graph can inform prompt engineering efforts to elicit reliable causal reasoning from language models. We believe investigating both directions represents an exciting direction for future work.
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APPENDIX
A SYNTHETIC DATA EXPERIMENTS
A.1 SELECTED SYNTHETIC PROMPTS
We an LLM to generate the events E. From the events, create ground truth causal graph G which is used to structure and inform the narrative sequence and causality. N is the corresponding narrative created by the LLM from G. To evaluate the LLM's performance, we extract causal graph, G', from the narrative N as produced by the LLM, and compare it with the ground truth causal graph G. In this context, refers the number of events generate, while A and B represent pairs of events queried for causal relationships. The task then becomes assessing whether event A causes event B All prompts, data processing steps, and results are included in the attached code. Furthermore all results are taken and aggregated over 5 random seeds, with the CI being taken after aggregation.
A.1.1 TOPOLOGICAL EXPERIMENT - GENERATING RANDOM EVENTS (E)
"generate и random distinct events"
A.1.2 PARAMETRIC EXPERIMENT -GENERATING A PAIR OF CAUSAL EVENTS (E)
"generate pair of events that cause each other. generate an event that causes another event, for example Cancer -> Death or Obesity -> Bad Heart Health. Make sure the event generated is not already in E'
This is repeated as many times as is necessary
A.1.3 PARAMETRIC EXPERIMENT GENERATING A PAIR OF ANTI-CAUSAL EVENTS (E)
"generate a pair events that are anticausal (an event causing the opposite of the normal effect), for example the first event could be cancer and the second event could be longer life because in reality, cancer causes a shorter life. Make sure the events generated are not already in F." This is repeated as many times as is necessary
A.1.4 FORWARD TOPOLOGICAL NARRATIVE (N)
"Output short narrative (use one sentence) that expresses the causal link [EI + E2]. By causal link, we mean that the sentence should convey that El directly caused E2. In other words, it should be clear from the narrative that E2 would not have happened had El not happened. Ensure that the words [E1, E2] are present in the new sentence and E1 appears before E2. Only output the new sentence." Repeat for all causal/anti-cant causall links
A.1.5 REVERSE TOPOLOGICAL NARRATIVE (N)
"Output a short narrative (use one sentence) that expresses the causal link [EI + E2]. By causal link, we mean that the sentence should convey that El directly caused E2. In other words, it should be clear from the narrative that E2 would not have happened had El not happened. Ensure that the words [E1, E2] are present in the new sentence and E2 appears before E1. Only output the new sentence." Repeat for all causal/anti causal links
A.1.6 STANDARD PROMPT
"Use this narrative as context. Did A cause B? Output your answer with < onswer > Yes/No< /answer >. The cause can be direct indirect."
A.1.7 IN-CONTEXT PROMPT
"Use this narrative N context. Did A cause B? Output your answer with answer Yes/No /answer >. The cause can be direct or indirect. An example narrative would be: Rains leads to plants growing. This then causes increased oxygen in the atmosphere. A potential question would be:
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does rain cause increased oxygen in the atmosphere? The answer would be Yes. Another example narrative would be: Increased oxygen in the atmosphere is because of plants growing. Plants grow because rain provides them essential nutrients. A potential question would be: does rain cause plants to grow? The answer would be Yes. Another example narrative would be: Rain leads plants to grow. Plants growing causes less oxygen in the atmosphere. A potential question would be: does rain cause more oxygen in the atmosphere? The answer would be No. Another example narrative would be: The city's pollution levels increased because factories expanded their production. A separate heatwave occurred due to seasonal climate patterns, unrelated to factory activity. A potential question would be: did factory expansion cause the heatwave? The answer would be No." "
A.1.8 NARRATIVE GRAPH PROMPT
"Use this narrative N and this causal ordering G' ((such that each item cause of every item after it, for example the first list item a cause of the third, fourth, fifth items etc)) as context, Did A cause B? Output your answer with onswer Yes/No /answer >. The cause can be direct or indirect."
A.2 PARAMETRIC GRAPH EXPERIMENT
Let's call the graph of parametric knowledge P. We then take the odd indexed events (Ist, 3rd etc) from P and place them in the first half of the causal ground truth graph G and the even indexed events (2nd, 4th etc) from P in the second half of G. This process is shown Figure
Example Illustration:
3
2
Legend
Parametric Knowledge
Ground Truth Causality for Narrative
Figure 6: Example illustration (right) of how G, the ground truth causality, is set up.
A.3 COMPLEX GRAPH CREATION
To generate ground-truth causal graph G with rich structure (colliders, forks, and a spanning chain), for each choice of size perform the following algorithm:
1. Node sampling. Draw n distinct events
{Ei, E2,
Em C E
uniformly at random without replacement.
2. Determine motif counts. (for n 2 4)
kmax
|n/2], leot Uniform (2, kmax).
Kool Uniform (1, kiot -1), kiork = Kiot
Kcol-
3. Collider creation. Repeat Reod times:
(a) Select two distinct "parent" nodes Pl, P2 from those not yet used in any motif.
(b) Select "child" node that neither p1 nor p2 and yet used as child.
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(c) Add edges
P1 - and
P2 + c,
thereby forming a collider at c.
4. Fork creation. Repeat Ktork times:
(a) Select "parent" node p from those not yet used.
(b) Select distinct "child" nodes c1, cg from the remaining unused nodes.
(c) Add edges
and p
c
forming fork with shared parent
5. Chain-connect remaining nodes. Let R. the set of nodes not yet involved any collider fork.
(a) Order R = -fri....................... Tm arbitrarily, then add chain edges
T3, .... Tm-1 + Tm
(b) To ensure the entire graph is connected, choose one node from among the previously used nodes (if any) and add
B REAL-WORLD CAUSAL GRAPHS
B. PROMPT TEMPLATES FOR NARRATIVE GENERATION
Recall that we have a ground truth causal chain graph of the form V1 + V/2 + ... + Vy from CauseNet that need verbalize into coherent narrative. For the semi-synthetic narratives, use the LLM (GPT-40) to do so one edge at a time, while ensuring that the newly verhalized edge logically follows the previous one. The following is the prompt template for generating the narratives in the topological order of the graph:
Output short narrative (use one or two sentences) that expresses the causal link [V/ + Vi+1] and logically follows this narrative:
Narrative for the previous edge V ViJ.
Ensure that combined sentences convey the causal chain [V:-1 - V, +
V++1 and that the words [V, V4+1 are present. Only output the newly generated narrative.
Similarly. we generate narratives in the reverse topological order of the graph by verbalizing edges in the reverse direction with the following prompt template:
Output a short narrative (use one or two sentences) that expresses the causal link [V: + Vi+1] and logically follows this narrative:
Narrative for the previous edge Vi+1 - V(+2).
Ensure that the combined sentences convey the causal chain [V- + V +
Vi+2] and that the words [V, V++1l are present. Only output the newly generated narrative.
For generating real- world narratives, for each edge V; + Vj. we use the set of sentences from CauseNer. Each edge in CauseNer is linked to multiple sentences from various sources. Picking sentence for each edge at random and concatenating them does not always lead to sensible narratives. To improve the quality of narratives, we use the following prompt to concatenate sentences for adjacent edges:
Consider the following sentences.
Sentence for edge V- - V4+1 ). Sentence for edge V441 - V ).
Do the sentences logically follow each other and express the causal chain [V; + Ve+ V/+2]? Answer with Yes or No.
17

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For verbalizing narratives in the topological order, for given graph V1 - V/2 we only use sentences such that the above prompt returns Yes for every pair of adjacent edges V; - V 2- This ensures that the narrative whole remains coherent and conveys the entire causal chain graph. We use a similar prompting strategy to verbalize narratives the reverse topological order.
VN.
B.2 ELICITING PARAMETRIC KNOWLEDGE
We ask the LLM "Does V, typically have causal (indirect or direct) effect VG?" and "Would it be atypical Vi had a (indirect or direct) causal effect on Vj?". the LLM answers "No" and "Yes" to those respective questions, would consider causal relationship between V; and V3 contradict the LLM's prior knowledge that learned from its pretraining corpora.
B.3 SEMI-SYNTHETIC AND REAL- WORLD COMPLEX GRAPH ALGORITHM
Let M = {(u, w}} be the set of real-world causal edges from CauseNet. For each target size {3, 93). we:
99,
1. Load CauseNet.
M {u, u~v in CauseNet).
2. Extract collider and fork motifs.
Colliders ((1,,22,c) (PI, c) E M, (22,c) E M, # P1). Forks = ((r, 02) (r,c1) E M, (2) E M, ei #c2)
3. Determine motif counts.
(1,0)
w.p. 0.5, w.p. 0.5.
If 3, (Hool, ktock)
(0.
(for 4)
kinax
|n/2|, Aicot Uniform (2, kmax);
Kool Uniform (1, hoot 1),
kiork = Kiot Kcol-
4. Select motil's.
Sample keol distinct triples from Colliders.
Sample kirk distinct triples from Forks.
Let the union of all nodes appearing these sampled triples.
5. Pad trim to size
If | > n, uniformly subsample n nodes from S.
If |S] n, add random "seed" nodes (not already in S) until [S]
6. Build ground-truth edges G C x S.
(a) Colliders: for each (P1, P2. c) chosen, add p1 - c and P2 + c.
(b) Forks:
for each (r, add r - C1 and - C2.
(c) Chains: for any remaining (u, v) E
with (u, v) E M and neither u nor used in the above, add u to ensure connectivity.
7. Narrative generation. For each (u-+)) E g: For the semi-synthetic case prompt the LLM generate sentence linking to using the forward topological ordering prompt.
For the real-world case: Find causal sentence linking and v in the Cause-Net database
18

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C REAL-WORLD COMPLEX GRAPH CREATION
C.I ADDITIONAL EXAMPLES OF THE GENERATED NARRATIVES
C.I.1 SEMI-SYNTIETIC NARRATIVES
Below, we present some examples of semi-synthetie narratives in the forward and reverse directions. The narrative in the forward direction for the chain higher prices - reduced demand - lower prices:
As higher prices swept through the market, consumers began to tighten their budgets, leading to a noticeable reduction in demand for many goods. As a result of the reduced demand, suppliers were forced lower prices in order attract buyers back to the market.
The narrative in the reverse order for the causal chain bankruptcy -> bad credit + rejection + anger:
The sting of rejection ignited a fire within her, transforming her hurt into a seething anger that demanded be felt. Her bad credit had led to the rejection she never saw coming, and now that sting of rejection ignited a fire within her, transforming her hurt into a seething anger that demanded to be felt. Her bankruptcy had left her with bad credit, a shadow that loomed over her every application, and now that sting of rejection ignited a fire within her, transforming her hurt into seething anger that demanded to be felt.
The narrative in the reverse order for the causal chain pollution + climate change + extreme weather events + natural disasters:
As extreme weather events become more frequent and severe, they increasingly lead to devastating natural disasters that disrupt communities and ecosystems alike. Climate change is driving the rise in extreme weather events, which in turn are causing unprecedented natural disasters that threaten the stability of communities and the health of ecosystems. Pollution is a major contributor to climate change, which is driving the rise in extreme weather events that threaten the stability of communities and the health of ecosystems.
C.1.2 REAL-WORLD NARRATIVES
Below, we present some examples of real-world narratives in the forward and reverse directions.
The narrative in the forward direction for the chain higher prices >> reduced demand - lower prices:
Higher prices generally lead to reduced demand. Lower prices, caused by reduced demand and increased competition for soybeans and corn, largely contributed to the overall bulk export decline.
The narrative in the reverse order for the causal chain bankruptcy - bad credit rejection + anger:
Embittered by an abusive upbringing, seething with resentment, irritated by others' failure to fulfill his or her superior sense of entitlement, and fuelled by anger resulting from rejection, the serial bully displays an obsessive, compulsive and self-gratifying urge to displace their uncontrolled aggression onto others whilst exhibiting an apparent lack of insight into their behavior and its effect on people around them. Bad credit normally leads rejection but now with bad credit secured loan, you can avail the loan of your choice. For example, if you are applying for a loan, the lender may reject your application on the basis of bad credit caused by bankruptcy.
The narrative in the reverse order for the causal chain pollution + climate change + extreme weather events natural disasters:
19

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In addition to forced migrations from rising seas, climate change is also increasing extreme weather events causing natural disasters such as cyclonic storms (hurr-- canes typhoons), floods and droughts. This is worsened by extreme weather events caused by climate change. This landmark bill would jump start the economy by creating millions of clean energy jobs, increase national security by reduc- ing dependence on foreign oil, and preserve the planet by reducing the pollution that causes climate change.
C.2 PROMPT TEMPLATES FOR ASSESSING CAUSAL REASONING
We the following template for the Direct prompting strategy:
Consider the following hypothetical narrative.
[narrative)
According to the hypothetical narrative, does (cause) have (direct indirect) causal effect on [effect]? Answer in Yes/No.
We use the following template for the Chain-of- Though (CoT) prompting strategy:
Consider the following hypothetical narrative. [narrative]
According to the hypothetical narrative, does {cause) have a (direct or indirect) causal effect {effect)? Think step-by-step and end your answer with <an- swer>>YesNo</answer>>
We use the following template extract chain graph from the narrative:
Consider the following hypothetical narrative.
[narrative]
According to the hypothetical narrative, construct a causal chain graph using the following nodes: nodes in random order ). Ensure that the graph con- tains all the given nodes and only output a single chain graph of the form <graph>nodel node2 node3 </graph>, Only output the graph between the <grapho-<graph>>tag.
C.3 NECESSARY COMPUTE
pretraining was done so no GPUs were needed. We used cloud based API calls to pre-trained models like ChatGPT, Anthropic and Llama. We estimate that for the synthetic portion, our API calls to ChatGPT, Anthropic and LLama took 10 hours each. For the semi-synthetic and real-world portion, we had roughly 10 hours of API calls for ChatGPT and Llama each. So in total, roughly 50 hours of API usage. As the majority of the computational burden fell on cloud based API calls, no significant CPU resources required either.
20

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D ADDITIONAL RESULTS- SYNTHETIC DATA
D.1 FORWARD VS REVERSE EXPERIMENTS
vs Acourecy
Accuracy
1.0
10
0.9
0.9
0.5
0.8
0.7
12 Size
Chain Size
Direction
Forward
CST Standard
Prompting Method
Criph O GrepS In-ootest
Directin
Forward
Standard
Prompting Nethed
Grash O Nam-Craph In-Canted
(a) Anthropic Claude 3,5 Sonnet
(b) Llama 3.1 8B
Liama 708 Forward vs Reverse Accuracy
GPT-5 Thinking Forward us Reverse Accuracy
3.0
10-
0.9
0.8
Acturacy
0.5
05-
0.4
12 Chain Size
0.4
16
20
Chain Size
Direction
Prompting Method
Dirction
Prompting Nethod
Formard
Reverse
COT
Standard
* Groph O Nan- In-Concust
Forward
Reverse
CUT
Standars
Guah O Nan-Graph I-Conted
(c) Llama 3.1 70B
(d) GPT-5 Thinking High Reasoning
Figure 7: (a) Anthropic Claude 3.5 Sonnet, (b) LLama 3.1 8B, (c) LLama 3.1 70B and (d) GPT-5 Thinking High Reasoning Test of the LLM's ability to reason on narratives written in the Forward and Reverse topological orientations. Chain size is the number of nodes in ground truth G. The "Graph" prompting method uses only the extracted graph G' to reason, "Narr-Graph" uses both the narrative and extracted graph, and "Standard, CoT, In-Context" all use only the narrative. Accuracy measures LLM answer agreement with G. The points in the graph are represented with a slight horizontal stagger around the relevant chain sizes (4,8,12 etc) for ease of visual understanding. We show a 95% CI.
In these graphs, we perform the Forward vs Reverse Experiments for (a) Anthropic Claude 3.5 Sonnet, (b) L.Lam 3.1 8B, (c) LLama 3.1 70B and (d)GPT-5 Thinking High Reasoning. Across scale of model sizes and reasoning capabilities, patterns emerge. We see that a consistent failure mode remains of models (small large) being much worse reasoning about reverse narratives than ones in the forward direction until we get to the reasoning model which closes the gap. We also notice that the reasoning model doesn't 't score perfectly in the forward regime like many of the non-reasoning models. The fact that it makes some mistakes that regime, while still doing well, is indicative of actual reasoning and not following simple shorteut.
21

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D.2 CAUSAL Vs CAUSAL EXPERIMENTS
Anthropic Causal vs Anti-Cuusal Accuracy
Liama 86 Causal vs Anti Coural Accuracy
Accurcc
0.8
0.6
0.0
12
16
20
12 Chain Size
16
20
Chain Size
Direction
Prompting
Direction
Prompting
Ant-Causs)
causal
Standard
Grash Garrd
Contoct
Causal
AntiCauunn
Graph O Grash I-Cortot
(a) Anthropic Claude Sonnet
(b) LLama 3.1 8B
Anti--Cun Accuracy
Accuracy
3.5
Accuracy
Chain Size
Chain Size
Direction
Caunal
Standard
Prompting Method
Grazh hannrg
Cortect
Direction
And
Caussl
Standars
Prompting Nathed
Graph O
Mam-Grash In-Centeet
(c) Llama 3.1 70B
(d) GPT-5 Thinking High Reasoning
Figure 8: (a) Anthropic Claude 3.5 Sonnet, (b) LLama 3.1 8B, (c) LLama 3.1 70B and (d) GPT-5 Thinking High Reasoning Test of the LLM's ability reason narratives that agree with parametric knowledge (Causal) and disagree with parametric knowledge (Anti-Causal). 95 % CI is shown.
In these graphs, we perform the Causal Anti-Causal Experiments for (a) Anthropic Claude 3.5 Sonnet, (b) LLama 3.1 8B, (c) LLama 3.1 70B and (d)GPT-5 Thinking High Reasoning. We see that larger models like GPT-4o and Claude 3.5 Sonnet perform far worse knowledge that conflicts with their pre-training compared to LLama models, possibly because they have been trained on so much can say that size of the model doesn't necessarily
more data than the LLama models. As such, translate into better performance for the failure modes we identified. What does seem to translate into significantly better performance is the amount of reasoning capability the model explicitly has.
22

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D.3 COMPLEX VS SIMPLE GRAPHS
Anthyopic Simple vs Complex Accuracy
Liama 5.1 Simple vs Complex Accuracy
Accuracy
0.8
0.6
0.0
12
16
20
12
16
20
Chain Size
Chain Size
Simple
Complex
Prompting
Grphh har
Varlart
Simple
Complex
Prompting
Gosh Nan Graph
Contoe
(a) Anthropic Claude Sonnet
(b) LLama 3.1 8B
Simple Accuracy
Complex Accuracy
3.5
Accuracy
Chain Size
Chain Size
Variant
Standard
Prompting Method
Gmph + Grans
Contest
Variant
Simple
Complex
Standars
Prompting Nathed
Grah <
Nam Grash Conteed
(c) Llama 3.1 70B
(d) GPT-5 Thinking High Reasoning
Figure 9: (a) Anthropic Claude 3.5 Sonnet, (b) LLama 3.1 8B, (c) LLama 3.1 70B and (d)GPT-5 Thinking High Reasoning Test of the LLM's ability reason narratives generated from Complex graphs as opposed to Simple chain graphs. 95 % CI is shown.
In these graphs, perform the Complex Simple Experiments for (a) Anthropic Claude 3.5 Sonnet, (b) LLama 3.1 8B, (c) LLama 3.1 70B and (d)GPT-5 Thinking High Reasoning. We see relatively similar performance across all models except for Llama 3.1 8B which has more variable performance. It's general inconsistency may be due to the fact that it a weaker model than the others presented.
23

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D.4 GRAPH EDIT DISTANCE
Here in Table 2 we compute the average graph edit distance per, using the implementation from the networkX package in python Abb--Aisehl et al. (2015)) where they define graph edit distance "It is defined as minimum cost of edit path (sequence of node and edge edit operations) transforming graph Gl graph isomorphic G2. 95% CIs given. We find GED be calibrated for different measure than graph accuracy, as for example one small change in the causal graph with GED drastically impact accuracy so two structures with similar GEDs can have drastically different accuracies.
Graph type
Models
Forward
Reverse
Causal
Anti-Causal
Complex
LLaMA 3.1 8B
.04(004.04)
.08(.08.08)
03(.03..03)
.03(.03.03)
.08(.07..1))
LLaMA 3.1 70B
.04(.04.004
.07(.07_007)
.02(022.02)
.02(.01_02)
,07(.06.009)
Claude 3.5 Sonnet
0(0,0)
.02(011.02))
0(0,0)
0(0,0)
.08(.07..1I)
ChatGPT-40
.03(00,,03))
.03(002.005)
.13(.12..15)
.09(.08..11)
0(0,0)
ChatGPT-5 (Thinking)
.02(.02,002)
.03(.03,033)
.02(.02.002)
.04(03,.05)
.04(.03.04)
Table 2: Graph Edit Distance by graph type for different models.
24

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E ADDITIONAL RESULTS SEMI-SYNTHETIC AND REAL WORLD DATA
E.1 FORWARD VS REVERSE LLAMA
Semi--yythhiic
10
2.-
0
0.0
6.6
Chain Size
Chain Size
Dioccion
Prompting Hethod OUT
Droszion
Prompting Method Standard O Graph
Semos
Savers
Figure 10: (LLama 3.1 8B) The accuracy of various prompting strategies (error bars denote 95% Cls) in the Semi-Synthetic and Real-World Regimes using CauseNet.
We observe that the accuracy is lower in the reverse direction in both regimes, and slightly lower yet in the real world regime. This is consistent with previous findings. The extracted graph does well.
E.2 PARAMETRIC EXPERIMENT LLAMA
Standard
CoT
Graph
Semi-synthetic
Without Conflict
88.4
83.7
99.5
With Conflict
61.4
57.9
98.2
Real-world
Without Conflict
81.6
79.2
95.1
With Conflict
48.8
49.9
93.2
Table (LLama 3.1 8B) The average accuracy across different narratives with the three prompting strategies partitioned by whether the cause-effect pairs conflict with the LLM's parametric knowledge (we omit the 95% CIs as they are smaller than 0.3).
We observe that the accuracy is drastically lower with conflicting information in both regimes, and slightly lower yet in the real world regime. This consistent with previous findings. We again see the graph doing very well.
25

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E.3 SIMPLE VS COMPLEX LLAMA
Loma Semii Synthetk Simple vs Complex
Llama Real World Simple vs Com plax
1.05
1.00
0.3
400
0.00
66
Chan Size
Chain Size
Varfart
Prompting Muthad Standed OCT Graph
Vor ant
Promptnng Wethod Zentard O CeT Gmo?
Figure 11: (LLama 3.1 8B) accuracy on narratives generated from Complex graphs as opposed to Simple chain graphs for semi-synthetic narratives (left) and real-world narratives (right). 95 % CI is shown.
We slight degradation of accuracy in the complex regime opposed the simple one, with the graph not fully recovering accuracy in the complex regime. This is consistent with previous findings.
26

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F HUMAN EVALUATION PROTOCOL AND RESULTS
We conducted human evaluation of narrative quality and graph faithfulness across the three regimes (synthetic, -ymi-synthetic, and real-world). For each regime, we sampled 150 unique narratives, yielding total of 450 narratives. Three external graduate-student reviewers (none of whom are authors) served as annotators.
For each regime, the 150 narratives were partitioned into three disjoint batches of 50 narratives. Each batch was assigned to a distinct pair of reviewers, so that every narrative was independently annotated by exactly two reviewers and each reviewer annotated 100 narratives per regime (300 narratives in total across all three regimes).
Tasks. For each narrative, annotators were given the list of node names and asked to:
1. Reconstruct the causal chain graph G' that they believed the narrative implied, and
2. Provide binary judgment of whether the narrative was fluent and coherent.
We say that narrative exactly reconstructed if the reconstructed graph G' matches the ground- truth graph G' exactly (all nodes present, all directions correct, and no extra edges). We report two notions of reconstruction accuracy:
Narrative-level accuracy: fraction of narratives in regime for which both annotators exactly reconstructed G.
Label-level accuracy: fraction of individual annotations (over all amnotator-narratte pairs) that exactly reconstructed G.
For agreement, we compute (i) the percentage narratives for which the two annotators produced identical graphs, and (ii) Cohen's 's x on the space of complete graph structures (treating each distinct chain as categorical label).
Reconstruction accuracy and agreement. Table summarizes reconstruction performance and annotator agreement. Across all regimes, narrative-level reconstruction accuracy is at least 97%, and label-level accuracy is close to 98% 99%..
Regime
# narr.
# ann.
Exact (N, %))
Exact (L, %)
Agree (%)
Synthetic
300
98.0
98.7
98.7
0.95
Semi-synthetic
150
300
97.3
98.3
98.0
0,93
Real-world
150
300
97.3
98.0
98.7
0.91
Table Human evaluation of graph reconstruction. "Exact (N, %)" the fraction of narratives for which both annotators exactly reconstructed the ground-truth graph G. "Exact (L., %)" is the fraction of individual annotations that exactly reconstructed G. "Agree (%)" is the fraction of narratives for which the two annotators produced identical graphs (whether correct or incorrect), x denotes Cohen's s kappa, a chance-corrected measure inter-annotator agreement computed over the space of complete graph structures.
Fluency judgments. For fluency, summarize ratings at the narrative level. A narrative is counted as fluent (both) if both annotators marked it fluent; is counted as fluent (2/ 1) if at least one annotator marked it fluent. Cohen's н is computed the binary fluent/non-fun fluent labels.
As shown in Table 5, the vast majority narratives all regimes are judged fluent, with slightly lower fluency rates in the real-world regime where source sentences can be more heterogeneous.
27

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Regime
Fluent (both, %)
Fluent (21, %)
Cohen's x: (fluency)
Synthetic
96.0
99.3
0.88
Semi-synthetic 94.0
98.7
0.86
Real-world
92.0
97.3
0.84
Table 5: Fluency judgments across regimes. "Fluent (both)" counts narratives where both annotators judged the narrative fluent: "Fluent (>1)" counts narratives where at least one annotator judged the narrative fluent.
Overall, human auditors almost always reconstruct the correct causal chain from our narratives and judge them be fluent, suggesting that our narrative generation procedures produce text that is both faithful to the underlying graph G and natural to read.
28


Reviews:
[{"review_id": "neqHAsDEVT", "reviewer": "Reviewer_C7XZ", "summary": "This paper analyzes how large language models assess causality in events described within synthetic, semi-synthetic, and real-world narratives. The authors examine the extent to which LLMs rely on their internal (parametric) knowledge, especially when their pre-existing causal beliefs conflict with information provided in the prompt. Additionally, the study investigates how the size and complexity of narratives and causal chains impact model performance. The authors also experiment with various prompting techniques and different types of causal chains to further understand these effects.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper is well written. While presenting multiple experimental settings, the authors succeed in delivering all necessary information and maintaining a coherent and accessible narrative throughout.\n- The experimental setup is particularly compelling. The authors systematically vary the conditions, carefully designing experiments that isolate and test one aspect at a time, which strengthens the validity of their reasoning.", "weaknesses": "**[W1]** One of the main limitations of this work is that the primary analysis and results focus almost exclusively on the GPT-4o model. While the authors include results for two additional models in the Appendix, they do not provide any substantive analysis or discussion of these models. Furthermore, not all experiments are conducted on both additional models. As a result, the paper is heavily GPT-4o-centric, which limits the generalizability of the findings and misses the opportunity to provide cross-model insights (such as how model size affects reliance on parametric knowledge).\n\n**[W2]**  The authors state that one of their motivations is to investigate the interaction between world knowledge and abstract logical reasoning, as well as the balance between reasoning and \"the right amount of world knowledge\". However, they do not clearly define what constitutes the \"right amount\" of world knowledge nor do they operationalize this concept in their experiments. It is also unclear how the causal and anti-causal relationships in their study map onto the dimensions of world knowledge and logical reasoning that they aim to explore. This claim would benefit from a clearer motivation and more precise definitions.\n\n**[W3]** The authors claim to evaluate the models' ability to reconstruct a causal graph from a narrative. However, they do not provide quantitative results or metrics for the performance of the causal graph extraction task.\n\n**[W4]** The paper treats anti-causality in a binary fashion, but in reality, anti-causal events can vary in the degree to which they conflict with parametric knowledge. Not all violations of common causal knowledge are equally significant. For example, the absence of a causal link between \"lack of sleep\" and \"happiness\" may be more OOD  and represent a stronger negative causality than the absence of a link between \"lack of sleep\" and \"morning shower\" where the events may simply be unrelated. In the former case, it might be desirable for the model to rely on its parametric knowledge for safety, while in the latter, reliance on the prompt would be preferable. These nuances and potential confounding factors in the data are not explored, which limits the actionability of this experiment's insights.", "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": "gdK50CBO9C", "reviewer": "Reviewer_npyf", "summary": "The paper aims to assess the *causal narrative understanding* ability of LLMs. The authors constructed various controlled experiments from synthetic, semi-synthetic, and real-world causal graphs and imbue them with short narratives by an LLM. They measured the LLMs' performance under various prompting strategies on the binary classification task of \"whether event \\$E_1\\$ causes event \\$E_2\\$ according to the given narrative\", with the difficulty varying on three separate axes: event ordering, graph complexity, and parametric consistency. \n\nThe main observations presented by the experiments are: LLMs suffer from the issue of *cognitive inertia*, prone to assuming that the events coming before are the cause of the events coming after; LLMs also like to take reasoning shortcuts, assuming that the causal relationships presented in the narrative would agree with its internal knowledge; LLMs perform worse when the causality graph grows complex, though not affected as much as the two former issues.", "questions": "1. Some counterfactual test cases might be ill-suited for current LLMs tuned for safety and truthfulness. For example, even if the narrative says \"taking poison prolongs one's lifespan\", the safety measures in the LLMs *could* trigger disagreement with the user. I suspect that some failing cases are caused by this issue.\n2. I would also like to see how the scale of the model influences the results. The leap from a \"tiny\" 8B model straight to GPT-4o seems too large.\n3. The models tested in the paper are all non-thinking models. I am curious about the performance of mainstream thinking models, possibly with varying thinking budgets. For the typical self-doubting and double-checking behavior of the thinking models, they might be able to achieve higher success rate than normal models with CoT prompting, in my intuition.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 3, "strengths": "* The paper discusses the interesting topic of complex causal understanding, and the results are curious. It seems that all the tested LLMs suffer from the defects of *cognitive inertia* and *taking reasoning shortcuts*. Future work on LLM reasoning could benefit from these observations.\n* The experiments are well-designed and thorough, covering various possible fail-modes of complex causal reasoning by LLMs.\n* The ideas in the paper are presented well and easy to follow.", "weaknesses": "* A concern on narrative generation: Pair-wise consistency does not necessarily imply global consistency, so it is questionable whether the whole narrative remains consistent in the test cases with more nodes. For example, in the example given in Section 4.1, around Line 358, since the second sentence uses a rather vague references of \"these accidents\" and just \"injury\", if there were a subsequent sentence following it, it may well be on the topic of car injuries instead of continuing on the topic of mining/factory injuries. This would make the logic chain unsound as a whole.\n* Some prompt constructions are dubious:\n  - A.1.8: Only \"yes\" cases are provided in the few-shot prompting text, which may bias the model.\n  - A.1.9, C.2.3: It is impossible to represent forks/joins in complex graphs (A → (B, C) → D) with a single chain. At the end of section 4.4, it is stated that \"this (complex graphs) is one area where extracting an explicit causal graph does not seem to significantly improve performance\" and this defect might be the direct cause.\n* Using the same LLM for problem generation but different ones for solving them might bias the results. For example, if the problems are generated by GPT-4o, it might benefit GPT-4o more than Claude 3.5 when evaluating, since the narrative style or internal knowledge could be more \"familiar\" to the generator itself. It is possible to mitigate this concern by using human-written narratives or by generating the narratives with different models.\n* Presentation nitpick: In Section 3.2, Subsection *\"Explicit Causal Graph...\"*, the second sentence reads wrong. I think you meant \"once $G'$ is extracted by the LLM, *the original narrative* is not given to the LLM again...\"", "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": "Ntto3aQ2dv", "reviewer": "Reviewer_QWBG", "summary": "This paper investigates how LLMs balance reasoning and world knowledge when performing causal inference over narratives. The authors claim that LLMs often rely on superficial heuristics/shortcuts such as memory of world knowledge from training rather than true casusal reasoning. The paper also evaluates a range of causal scenarios to further understand how LLMs handle causal tasks.", "questions": "1. Following up on the above, is there a chance that we are attributing the success to better prompt structure rather than true LLM reasoning capabilities?\n2. Could causal reasoning improve if models were trained explicitly on counterfactual data?\n3. What would manipulating event order and world knowledge conflicts together look like?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The experimental setup to separate reasoning difficulty from world knowledge conflict is novel\n2. The paper is well written and easy to read\n3. The evaluation of the proposal is thorough", "weaknesses": "1. More discussion on how the better performance is due to the proposed setup rather than \"just\" better prompting structure would help the paper\n2. A discussion on the concern about using LLMs to add synthetic data to judge causal performance would be helpful as well", "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": "SwsdxH5RMn", "reviewer": "Reviewer_BDoT", "summary": "The paper investigates causal reasoning over narratives in LLMs by probing two shortcuts: 1) ordering prior (events mentioned earlier are treated as causes) and 2) parametric/world-knowledge prior (typicality from pretraining). Using synthetic chains, synthetic general causal graphs, semi-synthetic (CauseNet chains verbalized by an LLM), and real-world (CauseNet sentences) narratives, the authors show that a) reverse order and atypical relations (vs the model’s prior) significantly degrade performance in causal narrative understanding and b) longer and more complex graphs (with forks/colliders) moderately degrade accuracy. They also report that extracting an estimated causal graph $G'$ and answering from the graph alone helps, but the benefit vanishes when the narrative is reintroduced.", "questions": "- L148: Synthetic generation (3.1, Setting): How are events linked into $G$, exactly? How did you ensure generated narratives do not contradict pre-existing LLM knowledge, a confounding failure mode that you identified in later sections?\n- L151-154: 50/2500 checks is too small. What were the selection criteria? Are there plans to extend the correctness/validity checks of the labels beyond the synthetic case?\n- Accuracy/Consistency formulas and CIs: Please state the exact formulas, unit of aggregation, seed handling, and CI method.\n- $G'$ quality evaluation: What are $G'$ edge precision/recall/F1, GED($G$, $G'$)?\n- Narr+Graph fusion diagnostics: Did you verify the model actually uses $G'$ efficiently under Narr+Graph? Please provide position swaps (graph before or after narrative), instruction variants (i.e., \"prioritize the graph\", vs neutral), format variants.\n- L245–254 This paragraph is very confusing as to which results it is discussing. Point to the exact figure/panel and report numerical deltas.\n- L261–263 / L345 / L355: Were these datasets examined? What audit criteria were used for semi-synthetic and real narratives (fluency, faithfulness to $G$, label correctness)?\n- Are there automatic sanity checks (and broader human audits) to catch cause/effect inversions when generating or stitching narratives?\n- The semi-synthetic and real world cases seem quite similar, intuitively. One would expect very similar performance. Why, then, are there differences in Figure 4?\n\nMinor:\n- L197 / L310 “randomly”: Specify distributions or use more precise wording.\n- Figure 2 (right) contains complex graphs, including forks and colliders, yet their \"chain size\" is computed. How is this calculated? Graph size usually denotes the number of edges, but I believe you are calculating the graph order here. Please clarify.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper tackles the crucial issue of LLMs' causal reasoning ability, a topic of significant interest.\n- There is clear factorization of failure modes (ordering vs typicality)\n- Breadth across synthetic chains, synthetic general causal graphs, semi-synthetic chains, and real chains.\n- A consistent empirical trend is uncovered: Reversing the ordering hurts, atypical/counter-intuitive chains confuse models.\n- Graph-only prompting consistenly mitigates most of these biases. This could provide a useful prompting insight: explicit $G'$ extraction reduces shortcutting when used alone.", "weaknesses": "- Metrics & aggregation left implicit. Accuracy = agreement with $G$ and Consistency = agreement with $G'$ are briefly stated but the paper lacks explicit formulas, units of aggregation, seed handling, and CI recipe in the main text.\n- Thin human validation; none beyond synthetic. Only 50/2500 synthetic narratives were audited, no inter-annotation agreement is reported, no audits were performed for semi-synthetic and real datasets, even though LLMs were used to modify them. This severely limits confidence in the labels.\n- Narrative vs graph interaction under-specified. Graph-only helps; Narr+Graph removes most of the Graph-only gain under reverse ordering. The fusion protocol isn't ablated (position of $G'$, format, instruction, relative length study). The mitigation claim (extract only $G'$) is fragile.\n- Complex-graph sampling is described in appendix only. There needs to be a brief summary of the fork/collider samplign and the chain-connect step in the main text.\n- Anti-Causal accuracy clustering around $\\approx 0.1-0.2$ for a binary task is alarming and warrants further investigation.", "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)