On the Eligibility of LLMs for Counterfactual Reasoning: A Decompositional Study

## Abstract

Abstract Counterfactual reasoning has emerged as a crucial technique for generalizing the reasoning capabilities of large language models (LLMs). By generating and analyzing counterfactual scenarios, researchers can assess the adaptability and reliability of model decision-making. Although prior work has shown that LLMs often struggle with counterfactual reasoning, it remains unclear which factors most significantly impede their performance across different tasks and modalities. In this paper, we propose a decompositional strategy that breaks down the counterfactual generation from causality construction to the reasoning over counterfactual interventions. To support decompositional analysis, we investigate 11 datasets spanning diverse tasks, including natural language understanding, mathematics, programming, and vision-language tasks. Through extensive evaluations, we characterize LLM behavior across each decompositional stage and identify how modality type and intermediate reasoning influence performance. By establishing a structured framework for analyzing counterfactual reasoning, this work contributes to the development of more reliable LLM-based reasoning systems and informs future elicitation strategies.

## 1 Introduction

Large language models (LLMs) have exhibited remarkable proficiency across a diverse range of tasks, including natural language understanding devlin2019bert; 10.1145/3711680 and multimodal reasoning hu2017learning; lu2022learn; yang2023mm. Despite these advancements, concerns persist regarding their reasoning and generalization capabilities. A particularly challenging aspect of model evaluation is Counterfactual Reasoning, i.e., the ability to adjust responses when presented with modified premises pearl2018book (e.g., What is the outcome in a hypothetical condition?). Investigating the counterfactual reasoning of LLMs provides an interpretable step to understand their adaptability under hypothetical alterations to input conditions gatfaithful; huang-etal-2024-clomo.

Prior studies have demonstrated that LLMs often struggle with counterfactual reasoning and frequently fail to maintain logical consistency or adjust to context shifts li-etal-2023-counterfactual; nguyen-etal-2024-llms; wang-etal-2024-survey. While these works highlight notable performance gaps, they lack a standardized framework for systematically analyzing and understanding counterfactual behaviors in LLMs. Consequently, it remains unclear what factors most significantly impact LLM performance in counterfactual scenarios. Furthermore, counterfactual reasoning has often been evaluated in a direct and monolithic manner, primarily by introducing interventions and assessing model responses li-etal-2023-counterfactual, without grounding the analysis in the underlying causal structure that gives rise to such interventions. This overlooks the foundational role of causal modeling. Specifically, the identification of causal variables and their dependencies are essential for understanding counterfactuals.

Figure: Figure 1: A workflow and illustrative example that decomposes LLM-based counterfactual reasoning into four stages: (1) identifying causal variables (e.g., whether web recommendation is shown), (2) constructing the causal graph (e.g., browsing history $\rightarrow$ a recommendation is shown), (3) specifying the counterfactual intervention (e.g., no recommendation shown), and (4) reasoning about the counterfactual outcome (e.g., less likely to purchase a product online).
Refer to caption: x1.png

To address these gaps, we outline a Decompositional Strategy that breaks down the analysis of counterfactual reasoning. Our approach departs from prior work that focuses solely on counterfactual generation. Instead, we begin by examining the causal structure of factual conditions, which serves as the necessary foundation for valid counterfactual reasoning.

As illustrated in Figure [1](https://arxiv.org/html/2505.11839v1#S1.F1), our methodology is outlined into four stages. First, we assess (i) whether LLMs can accurately identify the four variable groups critical to causal reasoning: Exposure, Covariate, Mediator, and Outcome. Next, we evaluate (ii) whether LLMs can correctly construct a corresponding causal graph in the form of a directed acyclic graph (DAG). Building on this causality modeling, we then study LLMs’ counterfactual reasoning abilities by evaluating (iii) whether they can identify the correct intervened variable (i.e., the Exposure), and (iv) whether they can accurately infer the counterfactual mediators and final outcomes by reasoning over the updated causal graph.

To support our decompositional study, we construct a benchmark by collecting and curating 11 counterfactual datasets across diverse tasks, including natural language understanding, mathematics, programming, and vision-language reasoning. We curate each dataset by extracting factual and counterfactual variables, identifying causal elements, and constructing corresponding causal graphs as reference structures for evaluation purpose. In experiments, we test the performance of leading LLMs across each decompositional stage to analyze their sufficiency in handling individual reasoning components. Based on the observed performance among these decomposed evaluations, we propose targeted improvements, such as integrating modality-specific function-calling interfaces within a tool-augmented learning paradigm, to address critical reasoning bottlenecks. Additionally, we evaluate the impact of different elicitation (prompting) strategies, including Chain-of-Thought (CoT) wei2022chain, Chain-of-Thought with Self-Consistency (CoT-SC) wang2022self, and Tree-of-Thought (ToT) 10.5555/3666122.3666639 reasoning. Collectively, our evaluations provide a solid step for understanding and enhancing LLMs in complex reasoning tasks and imaginative scenarios.

In summary, we make the following contributions:

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Decompositional Framework–We propose a decompositional strategy that spans from causal modeling to counterfactual reasoning, enabling a systematic evaluation of LLMs’ capabilities in understanding and performing counterfactual tasks.
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Benchmark Construction–We construct a comprehensive evaluation benchmark by curating causal structures and counterfactual instances across multiple domains. This benchmark standardizes decompositional evaluations and supports consistent analysis across tasks and modalities.
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Evaluation and Improvement Strategy–We evaluate leading LLMs under diverse tasks. By identifying LLMs’ capabilities in specific decompositional stage, we propose actionable strategies to improve LLMs’ counterfactual adaptability.

## 2 Related Work

Counterfactual Reasoning. A fundamental component of causal inference is to examine hypothetical scenarios, which addresses the question: What would have occurred had a particular factor or decision differed? pearl2018book This method facilitates causal analysis by comparing observed outcomes with those projected under alternative conditions.
Empirical research has demonstrated the broad applicability of counterfactual reasoning across multiple domains, including healthcare, business, and fairness gvozdenovic2021causal; kyrimi2025counterfactual; gow2016causal; kasirzadeh2021use; koonce2011judging.
This stands in contrast to many contemporary AI systems, which predominantly rely on statistical correlations while lacking robust capacities for abstract reasoning and causal inference jiao2024causal.

Counterfactual Reasoning in AI and NLP. Counterfactual reasoning has emerged as a powerful framework for enhancing model interpretability and causal understanding in AI and NLP. In medical AI, SyncTwin NEURIPS2021_19485224 proposed a counterfactual estimation framework that constructs synthetic patient data to predict potential outcomes under alternative treatments. In NLP, a Counterfactual Reasoning Model (CRM) feng-etal-2021-empowering is developed using LLMs to generate contrastive samples, improving sentiment analysis and inference tasks. There are also order-faithfulness metrics gatfaithful to evaluate causal explanations in black-box models. These contributions demonstrate the versatility of counterfactual methods in improving model transparency and reliability across domains.

LLMs and Elicitation. Recent advances have significantly enhanced LLMs’ reasoning capabilities through several elicitation (prompting) approaches. The introduction of Chain-of-Thought (CoT) prompting wei2022chain and its extensions, the Self-Consistency CoT wang2022self and Tree-of-Thought (ToT)  10.5555/3666122.3666639, have enabled more structured and reliable multi-step reasoning. Complementing these prompting techniques, reinforcement learning approaches using reward models lightman2023let have further improved reasoning quality through fine-grained supervision. These innovations collectively represent a paradigm shift from simple pattern recognition to deliberate, verifiable reasoning in LLMs.

## 3 Methodology: Decomposing Counterfactual Reasoning

This section presents our methodology for decomposing counterfactual reasoning. We begin by introducing foundational concepts in causality and counterfactual reasoning (Section [3.1](https://arxiv.org/html/2505.11839v1#S3.SS1)). Subsequently, we detail the evaluation tasks used to assess models (Section [3.2](https://arxiv.org/html/2505.11839v1#S3.SS2)) and our corresponding construction of benchmarks over multimodal datasets (Section [3.3](https://arxiv.org/html/2505.11839v1#S3.SS3)).

### 3.1 Preliminary: From Causality to Counterfactual Reasoning

Causality. Causality depicts the dependencies about how one variable influences another, i.e., the underlying causal effects. There are four types of variables commonly used in causal analysis: exposure, covariate, mediator, and outcome. Specifically:
(1) Exposure (or treatment, intervention, denoted $X$) refers to the action or condition imposed on a system;
(2) Outcome ($Y$) denotes the resulting response or effect influenced by the exposure;
(3) Covariate ($Z$) are pre-treatment variables that may influence both $X$ and $Y$;
(4) Mediator ($M$) lies on the causal pathway from $X$ to $Y$, representing intermediate mechanisms through which the exposure exerts its influence.

Causal Graph. The relationships among exposure, covariate(s), mediator(s), and outcome(s) can be formally represented using a directed acyclic graph (DAG), commonly named a causal graph that captures causal relationships, where the exposure $X$ influences the outcome $Y$ both directly and indirectly through a mediator $M$. Covariate $Z$ may affect the relationships among $X$, $M$, and $Y$.

Figure: Figure 2: (a) Causal graph structure and (b)(c) A factual/counterfactual example.
Refer to caption: x2.png

Counterfactual Reasoning. Counterfactual reasoning aims to answer:

The observed instance $(x,z,m,y)$ is also known as the factual case. In contrast, counterfactual reasoning seeks to determine the outcomes under an alternate intervention, that is, when $X=x^{\prime}$.

In causal graph, we assume a two-stage causal mechanism: (1) Mediator Function: $M=f_{M}(X,Z)$ and (2) Outcome Function: $Y=f_{Y}(X,M,Z)$. Then, the counterfactual outcome under an alternative exposure $x^{\prime}$ can be computed via:
$Y_{x^{\prime}}=f_{Y}\left(x^{\prime},\ f_{M}(x^{\prime},z),\ z\right)$,
where we simulate a new mediator value $M_{x^{\prime}}=f_{M}(x^{\prime},z)$ based on the counterfactual exposure $x^{\prime}$ and observed covariate $z$. Then, we predict the counterfactual outcome $Y_{x^{\prime}}$ using the counterfactual exposure $x^{\prime}$, the simulated mediator $M_{x^{\prime}}$, and the same covariate $z$.

### 3.2 Decompositional Evaluation Task

We define four evaluation tasks to systematically analyze LLMs’ counterfactual reasoning ability:

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Task I: Causal Variable Identification. Given inputs containing factual information, a model is required to identify the values of the causal variables $(X,Z,M,Y)$. This step serves as the foundation for subsequent causal modeling and counterfactual reasoning.
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Task II: Causal Graph Construction. Given the identified variables, the model is tasked with constructing a DAG that captures the causal relationships among them. This step evaluates the model’s ability to discover causal dependencies.
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Task III: Counterfactual Identification. Given a counterfactual query (e.g., “What if variable $X$ had been different?”), the LLM must identify the new value of (i.e., the intervention). This task evaluates whether the model can detect intervention in the counterfactual condition.
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Task IV: Outcome Reasoning. Based on the constructed causal graph and identified intervention, the model is prompted to predict the counterfactual outcome. This step measures whether the model can simulate the hypothetical scenario while respecting the underlying causal mechanisms.

Our evaluation spans these decompositional tasks across a variety of modalities (e.g., text, images, mathematical symbols, codes) and LLMs (e.g., GPT, Claude, Llama). Our ultimate goal is to quantify performance variations under different conditions and pinpoint how LLMs succeed or struggle in counterfactual reasoning.

### 3.3 Benchmarking Counterfactuals

Next, we introduce the datasets we leverage and the methodology we adopt to curate them for benchmarking LLMs’ counterfactual reasoning capabilities.

**Table 1: Summary of counterfactual benchmarks including data source, use case, presence of causal variables ($\CIRCLE$: present, $\LEFTcircle$: partially present), the definition of counterfactual condition, included modalities, and number of instances. Concrete examples are shown in Appendix [A](https://arxiv.org/html/2505.11839v1#A1).**
| Data | Use Case | Causal Variable | Counterfactual | Modality | Num |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $X$ | $Z$ | $M$ | $Y$ | Condition |  |  |  |  |
| CRASS frohberg2021crass | Question answering | $\CIRCLE$ | $\LEFTcircle$ | $\LEFTcircle$ | $\CIRCLE$ | “What if …” condition | Text | 274 |
| CLOMO huang2023clomo | Text logic parsing | $\CIRCLE$ | $\CIRCLE$ | $\CIRCLE$ | $\CIRCLE$ | New premise for textual statement | Text | 1,100 |
| RNN-Typology ravfogel2019studying | Text syntax parsing | $\CIRCLE$ | $\CIRCLE$ | $\CIRCLE$ | $\CIRCLE$ | New syntactic structure of sentence | Text | 584 |
| CVQA-Bool zhang2024if | Question answering | $\CIRCLE$ | $\LEFTcircle$ | $\LEFTcircle$ | $\CIRCLE$ | Hypothetical behavioral pattern | Text,Image | 1,130 |
| CVQA-Count zhang2024if | Numerical reasoning | $\CIRCLE$ | $\LEFTcircle$ | $\LEFTcircle$ | $\CIRCLE$ | Hypothetical numerical pattern | Text,Image | 2,011 |
| COCO le2023coco | Text-image matching | $\CIRCLE$ | $\CIRCLE$ | $\LEFTcircle$ | $\CIRCLE$ | “What if …” condition | Text,Image | 17,410 |
| Arithmetic wu2024reasoning | Mathematical reasoning | $\CIRCLE$ | $\CIRCLE$ | $\CIRCLE$ | $\CIRCLE$ | Change number base | Symbol | 6,000 |
| MalAlgoQA sonkar2024malalgoqa | Question Answering | $\CIRCLE$ | $\LEFTcircle$ | $\LEFTcircle$ | $\CIRCLE$ | “What if …” condition | Text,Symbol | 807 |
| HumanEval-Exe chen2021evaluating | Code Execution simulation | $\CIRCLE$ | $\LEFTcircle$ | $\CIRCLE$ | $\CIRCLE$ | Hypothetical coding criterion | Text,Code | 981 |
| Open-Critic vezora_code_preference_pairs_2024 | Code generation | $\CIRCLE$ | $\LEFTcircle$ | $\CIRCLE$ | $\CIRCLE$ | Hypothetical descriptive functions | Text,Code | 8,910 |
| Code-Preference vezora_code_preference_pairs_2024 | Code summarization | $\CIRCLE$ | $\LEFTcircle$ | $\LEFTcircle$ | $\CIRCLE$ | Hypothetical code structures | Text,Code | 9,389 |

Data Sources and Use Cases. As shown in Table [1](https://arxiv.org/html/2505.11839v1#S3.T1), we collect a diverse set of datasets to ensure broad coverage across various NLP tasks and modalities. The included use cases are:
(1) Question Answering, evaluated using CRASS frohberg2021crass, CVQA-Bool zhang2024if, and MalAlgoQA sonkar2024malalgoqa, which involve answering general-purpose textual or visually grounded questions;
(2) Text Parsing, using CLOMO huang2023clomo for logical structure reconstruction and RNN-Typolog ravfogel2019studying for syntactic structure understanding;
(3) Reasoning Tasks, with CVQA-count zhang2024if for numerical reasoning and Arithmetic wu2024reasoning for symbolic arithmetic computation;
(4) Multimodal Matching, represented by the COCO dataset le2023coco for image-text alignment;
(5) Code-based Tasks, including HumanEval-Exe chen2021evaluating for execution simulation, Open-Critic vezora_code_preference_pairs_2024 for generation, and Code-Preference vezora_code_preference_pairs_2024 for summarization.

These datasets span four modalities, natural language text, images, mathematical symbols, and code, that encompass diverse definitions of counterfactual interventions tailored to each task. Collectively, they support a comprehensive multimodal evaluation of LLMs’ abilities to reason under varied counterfactual settings and data types.

Our Curation. To adapt these datasets for counterfactual reasoning evaluation, we conduct a careful manual curation process to augment each instance with three additional aspects of information relevant to Tasks I–III (Section [3.2](https://arxiv.org/html/2505.11839v1#S3.SS2)). Specifically, we begin by identifying and annotating the causal variables $(X,Z,M,Y)$ from the original data, questions, or descriptions. Using these annotations, we construct a DAG to represent the underlying causal structure of each data instance.

All selected datasets naturally support counterfactual interventions (e.g., “What if…?” queries or hypothetical scenarios). For each instance, we parse and extract the intervened variables and, guided by the previously constructed DAG, annotate the corresponding counterfactual outcomes and construct a matched counterfactual graph. We provide the curated instances in Appendix [A](https://arxiv.org/html/2505.11839v1#A1).

This curation ensures that each benchmark instance is enriched with explicit causal and counterfactual structures. During the evaluation, we not only test LLMs’ overall counterfactual reasoning performance but also decompose the evaluation into Tasks I–III, leveraging our curated causal variables, DAGs, counterfactual interventions, and corresponding outcome graphs.

## 4 Experiment

We aim to empirically answer two research questions:
RQ1: How well do LLMs perform when their counterfactual reasoning is decomposed into distinct reasoning tasks?
RQ2: What auxiliary techniques (e.g., calling tools, prompting strategies) can improve LLMs’ counterfactual reasoning?
We defer the detailed settings into Appendix [C](https://arxiv.org/html/2505.11839v1#A3) and discussion of limitation into Appendix [D](https://arxiv.org/html/2505.11839v1#A4).

LLMs. Given the multimodal nature of counterfactual tasks, we evaluate several Visual LLMs, including GPT-4o openai2024gpt4o, Qwen-VL Qwen-VL, LLaMA-3.2-11B Meta2024Llama, Gemini-Pro gemini25pro, DeepSeek-VL lu2024deepseekvl.

Metrics. We use the F1 score for Tasks I, II, and IV, as they involve multiple instances (e.g., $M$, $Z$, or graph edges) that require set-level evaluation. For Task III, which typically involves a single intervention on $X$, we use accuracy to assess whether the LLM correctly identifies the intervened $X$.

### 4.1 LLM Performance on Decompositional Tasks (RQ 1 )

Setting. We evaluate LLMs independently on each decompositional task. For each task, we explicitly provide the ground-truth outputs from the preceding tasks to isolate and measure LLMs’ capabilities specific to that task. For example, when assessing models’ ability to construct causal graphs, we supply the original inputs along with the ground-truth causal variables, ensuring that the evaluation focuses solely on graph-building performance.

Task I: Causal Variable Identification. Table [2](https://arxiv.org/html/2505.11839v1#S4.T2) presents the performance of LLMs on identifying causal variables ($X,Z,M,Y$). We observe that model performance is strongly influenced by the modality of the dataset. Specifically, datasets involving more complex modalities—such as images, mathematical symbols, or code—tend to obviously reduce LLM accuracy (e.g., $<$0.7 F1 on Open-Critic), even when variables like $X,Z,Y$ are explicitly present in the context.

Interestingly, even within the text modality, LLMs show notable difficulty in identifying the implicit mediator $M$, which often requires reasoning about the underlying causal pathways connecting $X$, $Z$, and $Y$. This suggests that the challenge lies not only in the complexity of the input modality but also in the abstractness and inferential nature of the variable type itself. Together, these findings highlight the need for improved methods that enhance LLMs’ capacity to handle both cross-modal complexity and deeper causal reasoning.

**Table 2: LLMs’ performance (F1, scaled to 100%) in causal variable identification. We report mean$\pm$std across all instances for each variable, since $Z,M$ may include multiple entities.**
| Dataset | GPT-4o | Qwen-VL | Llama-3.2-11B | Gemini-Pro | DeepSeek-VL |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $v_{1}$ | $v_{2}$ | $v_{1}$ | $v_{2}$ | $v_{1}$ | $v_{2}$ | $v_{1}$ | $v_{2}$ | $v_{1}$ | $v_{2}$ |  |
| $v_{1}=X$ (Exposure), $v_{2}=Z$ (Covariate) |  |  |  |  |  |  |  |  |  |  |
| CRASS | 92.5±3.2 | 90.8±4.1 | 87.3±5.6 | 85.4±6.2 | 74.8±8.3 | 72.5±7.9 | 88.6±4.5 | 86.2±5.3 | 89.5±4.8 | 87.1±5.0 |
| CLOMO | 89.6±4.7 | 87.3±5.2 | 83.5±6.8 | 81.9±7.2 | 70.2±9.6 | 68.7±8.9 | 84.3±5.9 | 82.8±6.5 | 86.4±5.3 | 84.2±5.7 |
| RNN-Topo | 87.2±5.1 | 85.9±5.6 | 80.4±7.3 | 78.6±7.8 | 68.5±10.2 | 66.9±9.5 | 81.7±6.4 | 80.1±6.9 | 83.8±5.8 | 82.0±6.2 |
| CVQA-Bool | 76.4±8.6 | 74.8±9.1 | 72.3±9.7 | 70.5±10.2 | 56.8±12.4 | 54.2±13.1 | 68.5±10.8 | 66.9±11.5 | 70.7±9.4 | 68.3±10.1 |
| CVQA-Count | 72.1±9.5 | 70.6±10.2 | 68.9±10.6 | 67.2±11.3 | 52.6±13.7 | 50.8±14.2 | 65.8±11.5 | 63.7±12.3 | 67.4±10.3 | 65.1±11.2 |
| COCO | 70.5±10.8 | 68.9±11.5 | 67.2±11.9 | 65.4±12.6 | 48.3±15.2 | 46.7±15.8 | 62.6±12.9 | 60.9±13.7 | 65.9±11.6 | 63.2±12.5 |
| Arithmetic | 82.3±7.2 | 80.6±7.8 | 75.7±8.9 | 73.8±9.5 | 61.4±11.3 | 59.6±12.0 | 76.9±8.4 | 74.5±9.1 | 78.3±7.9 | 76.1±8.6 |
| MalAlgoQA | 79.8±7.9 | 77.5±8.4 | 72.9±9.5 | 70.6±10.1 | 58.7±12.2 | 56.4±12.9 | 73.5±9.0 | 71.2±9.7 | 75.9±8.5 | 73.4±9.2 |
| HumanEval-Exe | 68.9±11.4 | 66.5±12.1 | 63.7±12.8 | 61.9±13.6 | 45.8±16.3 | 43.2±17.1 | 59.6±13.9 | 57.3±14.7 | 62.1±12.5 | 59.8±13.4 |
| Open-Critic | 66.2±12.3 | 64.1±13.0 | 61.8±13.5 | 59.7±14.2 | 42.4±17.1 | 40.6±17.8 | 57.3±14.6 | 55.9±15.3 | 60.4±13.7 | 58.1±14.5 |
| Code-Preference | 77.5±11.9 | 65.3±12.6 | 72.9±13.1 | 60.5±13.9 | 54.3±16.7 | 42.1±17.4 | 68.4±14.3 | 66.5±15.0 | 61.2±13.1 | 59.3±13.9 |
| $v_{1}=M$ (Mediator), $v_{2}=Y$ (Outcome) |  |  |  |  |  |  |  |  |  |  |
| CRASS | 78.5±8.2 | 86.3±5.8 | 72.8±9.6 | 79.9±7.4 | 62.3±12.1 | 70.9±9.7 | 74.1±9.1 | 81.6±6.8 | 76.2±8.7 | 83.5±6.2 |
| CLOMO | 76.3±8.9 | 83.6±6.3 | 68.9±10.5 | 77.4±8.1 | 59.7±12.9 | 68.1±10.3 | 71.2±9.8 | 78.9±7.5 | 73.5±9.3 | 80.7±6.8 |
| RNN-Topo | 74.8±9.3 | 81.7±6.7 | 67.1±11.1 | 75.8±8.5 | 56.9±13.6 | 66.3±10.8 | 69.3±10.5 | 76.4±7.9 | 71.5±9.8 | 78.9±7.2 |
| CVQA-Bool | 64.8±12.1 | 72.5±9.7 | 59.7±13.8 | 66.9±11.3 | 48.2±16.9 | 56.3±14.2 | 57.3±14.1 | 63.2±12.5 | 60.5±13.2 | 68.4±10.7 |
| CVQA-Count | 61.3±13.0 | 68.7±10.5 | 56.8±14.6 | 63.6±12.1 | 44.5±17.8 | 53.2±15.1 | 54.7±15.0 | 60.4±13.3 | 57.9±14.1 | 65.1±11.6 |
| COCO | 59.7±13.7 | 66.3±11.2 | 54.2±15.3 | 61.8±12.8 | 41.9±18.5 | 50.4±15.9 | 52.8±15.7 | 58.3±14.1 | 55.7±14.8 | 62.6±12.4 |
| Arithmetic | 69.5±10.7 | 77.4±8.3 | 63.6±12.5 | 71.9±9.8 | 51.9±15.4 | 61.2±12.5 | 62.8±13.1 | 73.5±9.2 | 65.7±11.3 | 74.9±8.7 |
| MalAlgoQA | 67.6±11.3 | 74.8±8.9 | 61.5±13.1 | 69.3±10.4 | 49.7±16.1 | 58.9±13.2 | 60.2±13.8 | 70.5±10.0 | 63.8±12.0 | 72.3±9.3 |
| HumanEval-Exe | 57.8±14.5 | 64.3±12.0 | 51.9±16.2 | 59.7±13.6 | 39.8±19.3 | 48.5±16.7 | 49.7±16.5 | 57.0±14.8 | 53.6±15.5 | 60.5±13.1 |
| Open-Critic | 55.9±15.2 | 62.4±12.7 | 49.7±16.9 | 57.2±14.4 | 37.6±8.1 | 45.9±17.5 | 47.3±17.2 | 54.8±15.5 | 51.3±16.1 | 58.7±13.9 |
| Code-Preference | 56.8±14.9 | 67.5±12.3 | 50.4±16.5 | 78.6±14.0 | 38.4±19.7 | 67.2±17.0 | 48.2±16.9 | 76.1±15.1 | 52.5±15.8 | 59.8±13.5 |

Figure: Figure 3: Evaluation on causal graph construction. We evaluate F1 score to balance (i) whether the constructed edges under one category (e.g., $X\rightarrow M$) is correctly constructed if the ($X,Z,M,Y$) are already given. Additional results for all other datasets at Figure [5](https://arxiv.org/html/2505.11839v1#A2.F5).
Refer to caption: x3.png

Task II: Causal Graph Construction.
As described in our experimental setup, we isolate each decompositional step by providing the ground-truth outputs of preceding steps as inputs. For causal graph construction, we supply the identified variables $X,Z,M,Y$ and prompt LLMs to construct the corresponding counterfactual graph. The results are presented in Figures [3](https://arxiv.org/html/2505.11839v1#S4.F3) and [5](https://arxiv.org/html/2505.11839v1#A2.F5). Notably, the overall performance mostly exceeds 0.9 F1 scores, indicating that LLMs can accurately construct graph edges. Moreover, the impact of dataset modality and variable types (e.g., explicit $Z$ vs. implicit $M$) appears to be minimal in this step. We attribute this to the rule-based nature of causal graph construction: since causal graph structures are well-defined (as shown in Figure [2](https://arxiv.org/html/2505.11839v1#S3.F2)), it is relatively less challenging for LLMs to apply construction rules and generate the correct causal relationships.

Figure: Figure 4: Evaluation of LLMs’ accuracy in identifying the correct intervention (i.e., the counterfactual value of $X$). Additional results for all other datasets are provided in Figure [6](https://arxiv.org/html/2505.11839v1#A2.F6).
Refer to caption: x4.png

Task III: Counterfactual Identification. Next, we evaluate LLMs’ capability in identifying interventions, i.e., determining the counterfactual values of $X$ (i.e., $X^{\prime}$). As shown in Figures [4](https://arxiv.org/html/2505.11839v1#S4.F4) and [6](https://arxiv.org/html/2505.11839v1#A2.F6), the experimental results indicate that LLMs are generally effective at recognizing the counterfactual values of $X$ across most datasets and modalities. This demonstrates that LLMs have a solid grasp of pinpointing intervention points within the context. However, note that the task remains relatively isolated and does not challenge the model’s ability to propagate the effects of the intervention through downstream variables (e.g., $M^{\prime},Y^{\prime}$), which we address in Task IV.

Task IV: Outcome Reasoning.
In this final task, we evaluate LLMs’ ability to infer the mediator ($M^{\prime}$) and outcome ($Y^{\prime}$) under a counterfactual intervention. As shown in Table [3](https://arxiv.org/html/2505.11839v1#S4.T3), LLMs consistently exhibit insufficient performance in inferring these implicit variables across all datasets. Notably, since both $M^{\prime}$ and $Y^{\prime}$ are implicit under the counterfactual condition (whereas only $M$ is implicit in the factual condition), this result suggests that LLMs lack sufficient capacity to reason over causal chains, even when the underlying structure is explicitly provided.

Furthermore, the results reveal a similar trend in modality influence as previously observed in Table [2](https://arxiv.org/html/2505.11839v1#S4.T2), where complex, non-text modalities hinder effective reasoning. For example, in the code execution task (HumanEval-Exe), GPT-4o achieves only $<$0.65 F1 scores, which is notably lower than its performance on text-based tasks such as CLOMO.

**Table 3: LLM performance (F1) in reasoning the counterfactual mediator ($M^{\prime}$) and outcome ($Y^{\prime}$).**
| Dataset | GPT-4o | Qwen-VL | Llama-3.2-11B | Gemini-Pro | DeepSeek-VL |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $M^{\prime}$ | $Y^{\prime}$ | $M^{\prime}$ | $Y^{\prime}$ | $M^{\prime}$ | $Y^{\prime}$ | $M^{\prime}$ | $Y^{\prime}$ | $M^{\prime}$ | $Y^{\prime}$ |  |
| CRASS | 85.7±4.8 | 80.3±6.9 | 80.5±6.7 | 73.9±8.5 | 68.4±10.3 | 61.6±12.8 | 81.7±6.3 | 75.2±8.1 | 82.9±5.5 | 77.1±7.4 |
| CLOMO | 83.6±5.2 | 77.9±7.3 | 77.8±7.2 | 71.6±9.0 | 65.8±10.9 | 59.3±13.4 | 79.3±6.7 | 72.8±8.6 | 80.5±6.0 | 74.3±7.9 |
| RNN-Topo | 81.3±5.7 | 75.8±7.8 | 75.6±7.8 | 69.4±9.5 | 63.7±11.5 | 57.2±14.1 | 77.1±7.2 | 70.6±9.1 | 78.3±6.5 | 72.0±8.4 |
| CVQA-Bool | 71.8±9.3 | 65.2±11.4 | 65.4±11.2 | 58.6±13.3 | 53.1±14.8 | 46.5±17.2 | 63.2±12.1 | 56.8±14.0 | 66.7±10.4 | 59.8±12.5 |
| CVQA-Count | 68.4±10.1 | 61.9±12.2 | 62.2±12.0 | 55.7±14.1 | 50.3±15.6 | 43.8±18.0 | 60.1±12.9 | 53.8±14.9 | 63.5±11.2 | 56.9±13.3 |
| COCO | 66.2±10.7 | 59.5±12.9 | 60.1±12.7 | 53.4±14.8 | 48.0±16.3 | 41.5±18.7 | 57.8±13.6 | 51.5±15.6 | 61.3±11.9 | 54.6±14.0 |
| Arithmetic | 77.1±7.6 | 71.3±9.7 | 69.8±9.5 | 63.2±11.6 | 57.8±13.2 | 51.2±15.8 | 72.1±9.0 | 65.4±11.1 | 74.0±8.3 | 67.5±10.4 |
| MalAlgoQA | 74.8±8.2 | 68.9±10.3 | 67.5±10.1 | 60.9±12.2 | 55.9±13.8 | 49.3±16.4 | 69.6±9.7 | 62.9±11.8 | 71.8±8.9 | 65.1±11.0 |
| HumanEval-Exe | 64.4±11.3 | 57.6±13.5 | 58.2±13.3 | 51.5±15.4 | 46.1±17.0 | 39.6±19.4 | 55.8±14.2 | 49.4±16.2 | 59.4±12.5 | 52.7±14.7 |
| Open-Critic | 62.3±11.9 | 55.5±14.1 | 56.0±13.9 | 49.4±16.0 | 44.2±17.7 | 37.8±20.1 | 53.7±14.8 | 47.3±16.9 | 57.2±13.1 | 50.6±15.3 |
| Code-Preference | 63.5±11.6 | 56.7±13.8 | 57.1±13.6 | 50.4±15.7 | 45.1±17.3 | 38.7±19.8 | 54.7±14.5 | 48.3±16.5 | 58.3±12.8 | 51.6±15.0 |

### 4.2 Auxiliary Techniques to Improve Counterfactual Reasoning (RQ 2 )

**Table 4: Improvement in LLM performance (comparing with Table [2](https://arxiv.org/html/2505.11839v1#S4.T2)) for identifying explicit causal variables. Results are reported on six representative datasets spanning all major modalities.**
| Dataset | GPT-4o | Llama-3.2-11B | Gemini-Pro |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $X$ | $Z$ | $Y$ | $X$ | $Z$ | $Y$ | $X$ | $Z$ | $Y$ |  |
| CRASS | $+$5.7 | $+$6.3 | $+$5.2 | $+$16.5 | $+$12.2 | $+$12.6 | $+$6.6 | $+$5.3 | $+$7.1 |
| CLOMO | $+$4.4 | $+$4.8 | $+$7.6 | $+$22.6 | $+$15.2 | $+$18.3 | $+$6.2 | $+$9.4 | $+$15.5 |
| CVQA-Count | $+$17.3 | $+$15.5 | $+$17.8 | $+$33.6 | $+$27.5 | $+$25.6 | $+$18.1 | $+$15.7 | $+$19.3 |
| COCO | $+$5.2 | $+$2.8 | $+$4.1 | $+$7.8 | $+$6.4 | $+$10.3 | $+$6.9 | $+$3.8 | $+$5.2 |
| MalAlgoQA | $+$3.9 | $+$5.6 | $+$2.3 | $+$13.7 | $+$10.9 | $+$7.2 | $+$12.4 | $+$8.5 | $+$8.3 |
| Open-Critic | $+$12.5 | $+$14.2 | $+$6.7 | $+$18.9 | $+$5.8 | $+$1.7 | $+$7.3 | $+$6.1 | $+$9.8 |

Given the insights from previous evaluations, we aim to correspondingly address the limitations arising from multimodal complexity and intermediate reasoning, we propose augmenting LLMs with two auxiliary techniques: (i) tool-augmented execution and (ii) advanced elicitation strategies.

#### 4.2.1 Tool-Augmented Execution in Explicit Variable Identification Across Modalities

Settings. To enhance LLM performance in identifying explicit variables ($X,Z,Y$) across different modalities, we adopt a tool-augmented approach, where the LLM dynamically calls additional specialized tools to assist in entity identification, identical to a named entity recognition (NER) paradigm. We leverage several pretrained models from tailored to the multimodality of datasets:

- –
Text-based NER: We use bert-base-NER DBLP:journals/corr/abs-1810-04805; tjong-kim-sang-de-meulder-2003-introduction to identify candidate entities in text-based data, including mathematical symbols.
–
Vision-based NER: We employ grounding-dino-base liu2023grounding to detect all relevant objects in images and generate focused regions by masking out irrelevant backgrounds.
–
Code analysis: We adopt GraphCodeBERT guo2020graphcodebert to extract functions, variables, and control structures within programming tasks.

After identifying candidate entities across each modality, we prompt the LLM to refine and filter the final set of explicit variables ($X,Z,Y$) according to the formal definitions provided in Section [3.1](https://arxiv.org/html/2505.11839v1#S3.SS1).

Experimental Results. We randomly select three representative LLMs and conduct experiments across multiple datasets. As shown in Table [4](https://arxiv.org/html/2505.11839v1#S4.T4), tool-augmented execution consistently improves LLM performance in identifying explicit causal variables ($X,Z,Y$) across all modalities. For example, by leveraging GraphCodeBERT to parse code structures and forward the results to Llama, which gains a clearer understanding of programming logic and achieves an F1 improvement up to 0.189. Similarly, in the vision modality, the object detector grounding-dino-base assists by generating a set of candidate visual objects, which GPT-4o can then contextualize and compose into a coherent factual variable. For instance, detected objects like “Woman,” “Knife,” and “Apple” can be effectively integrated by GPT-4o into the causal expression: “A woman cuts an apple with a knife.”

These results demonstrate that tool-augmented learning effectively mitigates modality-specific bottlenecks by offloading low-level entity recognition to specialized models, allowing the LLM to focus on higher-level reasoning. Looking forward, there is potential to explore alternative tool configurations that may yield comparable or even superior performance. Additionally, future work may explore multi-agent frameworks with specialized agents to collaboratively handle different variable categories.

#### 4.2.2 Advanced Elicitation Strategies for Reasoning over Implicit Variables

Settings. To enhance LLM reasoning over implicit variables, particularly factual $M$ and counterfactual $M^{\prime},Y^{\prime}$, we implement advanced elicitation strategies that guide the model through more structured reasoning. Specifically, we apply Chain-of-Thought (CoT) wei2022chain, Chain-of-Thought with Self-Consistency (CoT-SC) wang2022self, and Tree-of-Thought (ToT) 10.5555/3666122.3666639:

- –
CoT: given pre-determined explicit variables, LLMs are encouraged to infer intermediate variables step-by-step.
–
CoT-SC: LLMs are prompted to generate multiple reasoning paths and select the final answer via majority voting or consensus.
–
ToT: LLMs are prompted to explore multiple parallel reasoning paths in a branching structure and evaluate candidate outputs based on intermediate criteria.

In implementation, both CoT-SC and ToT are executed with $k$=5 sampled reasoning paths. ToT further evaluates candidate outputs by scoring their textual similarity using BERTScore bert-score, specifically assessing how well the intermediate results align with the original task statement.

**Table 5: Improvement of LLM performance (F1 score) in reasoning implicit variables.**
| Dataset | Elicitation | GPT-4o | Llama-3.2-11B | Gemini-Pro |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $M$ | $M^{\prime}$ | $Y^{\prime}$ | $M$ | $M^{\prime}$ | $Y^{\prime}$ | $M$ | $M^{\prime}$ | $Y^{\prime}$ |  |  |
|  | CoT | $+$5.3 | $+$3.7 | $+$2.9 | $+$8.6 | $+$6.2 | $+$4.5 | $+$6.8 | $+$4.5 | $+$3.2 |
| CRASS | CoT-SC | $+$5.1 | $+$4.7 | $+$5.3 | $+$11.3 | $+$8.9 | $+$6.4 | $+$9.4 | $+$6.8 | $+$5.0 |
|  | ToT | $+$6.5 | $+$4.9 | $+$3.8 | $+$9.7 | $+$8.1 | $+$5.2 | $+$8.2 | $+$5.9 | $+$4.3 |
|  | CoT | $+$6.1 | $+$4.6 | $+$3.3 | $+$8.9 | $+$7.5 | $+$5.3 | $+$7.6 | $+$5.3 | $+$3.9 |
| CLOMO | CoT-SC | $+$6.7 | $+$4.9 | $+$3.9 | $+$10.6 | $+$8.3 | $+$6.0 | $+$8.9 | $+$6.5 | $+$4.7 |
|  | ToT | $+$9.8 | $+$3.5 | $+$2.6 | $+$7.9 | $+$5.8 | $+$4.2 | $+$12.4 | $+$4.2 | $+$3.0 |
|  | CoT | $+$5.4 | $+$4.1 | $+$3.2 | $+$8.7 | $+$6.8 | $+$4.9 | $+$7.3 | $+$5.4 | $+$3.8 |
| CVQA-Count | CoT-SC | $+$3.8 | $+$3.9 | $+$2.1 | $+$6.5 | $+$7.7 | $+$6.3 | $+$12.2 | $+$7.6 | $+$4.5 |
|  | ToT | $+$4.6 | $+$3.5 | $+$2.8 | $+$7.1 | $+$5.9 | $+$4.2 | $+$6.1 | $+$4.5 | $+$3.1 |
|  | CoT | $+$3.5 | $+$2.6 | $+$1.9 | $+$6.2 | $+$4.3 | $+$3.0 | $+$4.9 | $+$3.4 | $+$2.3 |
| COCO | CoT-SC | $+$5.1 | $+$3.8 | $+$2.9 | $+$8.3 | $+$6.4 | $+$4.5 | $+$6.9 | $+$5.1 | $+$3.6 |
|  | ToT | $+$4.2 | $+$3.2 | $+$2.4 | $+$7.4 | $+$5.8 | $+$3.8 | $+$5.6 | $+$4.3 | $+$3.0 |
|  | CoT | $+$4.3 | $+$3.2 | $+$2.4 | $+$7.2 | $+$5.2 | $+$3.6 | $+$5.8 | $+$4.0 | $+$2.7 |
| MalAlgoQA | CoT-SC | $+$5.2 | $+$4.0 | $+$3.1 | $+$8.3 | $+$6.6 | $+$4.7 | $+$7.2 | $+$5.2 | $+$3.7 |
|  | ToT | $+$5.9 | $+$4.5 | $+$3.5 | $+$9.5 | $+$7.6 | $+$5.4 | $+$8.1 | $+$6.0 | $+$4.3 |
|  | CoT | $+$3.2 | $+$2.4 | $+$1.7 | $+$5.8 | $+$4.0 | $+$2.8 | $+$4.5 | $+$3.1 | $+$2.1 |
| Open-Critic | CoT-SC | $+$3.9 | $+$3.0 | $+$2.3 | $+$6.5 | $+$4.8 | $+$3.5 | $+$5.0 | $+$3.5 | $+$2.7 |
|  | ToT | $+$4.7 | $+$3.5 | $+$2.6 | $+$7.7 | $+$6.0 | $+$4.2 | $+$6.3 | $+$4.7 | $+$3.4 |

Experimental Results. Our experimental results in Table [5](https://arxiv.org/html/2505.11839v1#S4.T5) show that advanced elicitation strategies generally lead to improved performance in reasoning over implicit variables ($M,M^{\prime},Y^{\prime}$) despite factual or counterfactual cases. However, we also observe that more complex prompting strategies (e.g., CoT-SC and ToT) can sometimes perform slightly worse than simpler approaches (e.g., CoT). While these advanced methods encourage more exhaustive exploration of reasoning paths, they may also induce overthinking behavior in LLMs, leading the model to introduce unnecessary causal links or misinterpret the underlying problem structure. For instance, consider the following context “A person is running a marathon and collapses.” with expected mediator “Dehydration”. While CoT and CoT-SC strategies correctly identify the mediator, ToT leads LLMs to overanalyze and identify “lack of training” or “overexertion” as the mediator. These choices, although related, are not directly supported by the input data and reflect an over-extension of the reasoning process.

The over-qualification of elicitation strategies (e.g., ToT) highlights that, while advanced prompting techniques can improve reasoning capabilities, they may also introduce complexities that divert the model from the most straightforward and contextually supported causal pathways. Therefore, it’s crucial to balance the reasoning depth while maintaining alignment with the input data.

## 5 Conclusion

This work provides a decompositional framework for evaluating counterfactual reasoning in LLMs. We collect a set of datasets across multimodalities, and then curate them with reference causal variables, structured graphs, and counterfactual intervention. Next, we use our curated dataset to study the reasoning process into distinct stages from causal variable identification to outcome inference. We uncover the qualifications of current LLMs in counterfactual reasoning and where they fall short. Based on experimental insights, we further propose improvements to offer actionable insights for enhancing LLM reasoning, particularly in multimodality and implicit reasoning settings.

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## Appendix A Complementary Information of Causality and Causal Graph

This appendix presents a concise overview of each dataset, followed by its causal structure and graphical representation, alongside a concrete example. For each dataset, we identify the four variable types—Exposure, Covariate, Mediator, and Outcome—and distinguish their roles in both factual and counterfactual scenarios, illustrating each with directed edges in the corresponding causal graph. In addition, we include a sample prompt used to generate responses on a simple text-parsing Q&A task dataset.

CRASS Example. The Counterfactual Reasoning Assessment for Structured Scenarios (CRASS) dataset is designed to evaluate whether language models can reason about hypothetical alternatives to factual events. Each example in CRASS presents a factual scenario (e.g., “A woman opens a treasure chest”) followed by a counterfactual question (e.g., “What would have happened if the woman had not opened the treasure chest?”). Models are asked to select the most logically consistent outcome from multiple-choice options, such as “The treasure chest would have remained closed”, which is labeled as correct. The following displays a full example:

CRASS Prompt.

CRASS Causality & Causal Graph.

CLOMO Example. The Counterfactual Logical Modification (CLOMO) dataset is designed to evaluate whether large language models can perform controlled, counterfactual edits to natural language arguments in a logically coherent way. Each example presents a base argument and two premises: Premise 1 has a logical sensitivity with the original argument, while Premise 2 does not. The model is instructed to modify the argument such that Premise 2 has a logical sensitivity with the original argument, while Premise 1 no longer is. For instance, given an argument attributing the rise in gasoline prices fully to government policies, the model must produce a revised version (e.g., changing “fully responsible” to “partly leads”) of an argument that shifts logical sensitivity from one premise to another without introducing new claims. The following displays a full example:

CLOMO Causality & Causal Graph.

RNN-Typology Example. This is a synthetic dataset contains sentence pairs that reflect syntactic alterations to word orders (e.g., converting English from subject-verb-object (SVO) to subject-object-verb (SOV) order). For example, the factual sentence “Tim saw Lucas.” (SVO) is transformed to its SOV equivalent “Tim Lucas saw.”.

RNN-Typology Causality & Causal Graph.

CVQA-Bool Example. Counterfactual Visual Question Answering(CVQA) is designed to assess the ability of vision-language models to perform counterfactual reasoning over images. Each example presents a factual visual query-answer pair (e.g., “Is there a red sandal here?” → yes) grounded in a COCO image, along with a corresponding counterfactual query that modifies a key visual condition (e.g., “Would there be a red sandal here if all shoes were removed?” → no). The task requires the model to infer changes in object presence or relationships under hypothetical alterations to the scene. The dataset focuses on a boolean query type. The following displays an example:

| real image | query | answer | new query | new answer | type |
| --- | --- | --- | --- | --- | --- |
|  | Is there a red sandal here? | yes | Would there be a red sandal here if all shoes were removed? | no | boolean |

CVQA-Bool Causality & Causal Graph.

CVQA-Count Example. Visual Counterfactual Query Dataset (CVQA) also evaluates whether language models can perform a direct or indirect numerical counterfactual reasoning grounded in visual inputs. Each example consists of a factual visual question (e.g., “How many plates are there?” → 1) paired with a corresponding counterfactual query that modifies the quantity in a clearly defined way (e.g., “How many plates would there be if 2 more plates were added?” → 3). The model must integrate visual perception (e.g., detecting a single white plate in an image) with numerical logic (e.g., adding 2) to produce the correct answer. The dataset focuses on a counting query type. The following displays an example:

| real image | query | answer | new query | new answer | type |
| --- | --- | --- | --- | --- | --- |
|  | How many plates are there | 1 | How many plates would there be if 2 more plates were added? | 3 | direct counting |

CVQA-Count Causality & Causal Graph.

COCO Example. Common Objects in Context(COCO) dataset provides automatically constructed counterfactual examples for evaluating multimodal reasoning in image-text pairs. Each instance contains two images and two near-identical captions that differ only in a key noun (e.g., “A big burly grizzly bear is shown with grass in the background” vs. “A big burly grizzly bear is shown with deer in the background”). The dataset is designed to test whether models can detect minimal semantic changes and determine whether the new image visually aligns with the counterfactual caption. The goal is to assess visual-textual consistency and a model’s sensitivity to causal or identity-based alterations in structured multimodal contexts. The following displays an example:

| Factual Caption | Image 0 | Counterfactual Caption | Image 1 |
| --- | --- | --- | --- |
| A big burly grizzly bear is shown with grass in the background. |  | A big burly grizzly bear is shown with deer in the background. |  |

COCO Causality & Causal Graph.

Arithmetic Example. Base-computation Arithmetic dataset evaluates counterfactual numerical reasoning by testing arithmetic operations across multiple numeral systems(e.g. Base-8, 9, 10, 11, 16). Each example pairs a factual base-10 calculation with a counterfactual alternate-base computation (e.g., base-8: $14_{8}+57_{8}=73_{8}$, base-16: $EC_{16}+DD_{16}=1C9_{16}$). The dataset includes inputs (num1, num2), the numeral system (e.g., "8" for octal, "16" for hexadecimal), and the base-specific result (addrst). It assesses models’ ability to adapt numeral system transitions and consistency in counterfactual reasoning.
The following display a base-8 computation example and a base-16 example:

Base-8 Arithmetic Causality & Causal Graph.

Base-16 Arithmetic Causality & Causal Graph.

MalAlgoQA Example (Malformed Algorithmic Question Answering (MalAlgoQA) dataset is designed intentionally including factual and counterfactual rationales between multiple-choice question answering to validate a language model’s ability to discern sound reasoning in the presence of rationales(factual or counterfactual). Each question is presented alongside a factual rationale that supports the correct answer (e.g., “Correctly ordered the values from greatest to least: 276, 254, 237, 235.” → C), and is paired with counterfactual rationales (e.g., “Ordered least to greatest”) that correspond to plausible but incorrect or altered answers (e.g., A). Each example is decomposed into factual and counterfactual role pairs, allowing researchers to assess how changes in reasoning paths (rationales) lead to different answer choices. The following display an example of raw data and its decomposed data points:

Take the first decomposed sample as an example showing MalAlgoQA Causality & Causal Graph.

HumanEval-Exe Example This dataset performs a programming-related task: code execution. It is designed to probe the ability of code-execution language models to perform counterfactual reasoning in the context of program behavior. Each example consists of a function definition and a test case input, and the model is asked to predict the output under a factual assumption (e.g., Python’s default 0-based indexing). The example is paired with a counterfactual version of the same test case, where a hypothetical condition is introduced—such as switching to 1-based indexing. The model must then predict the corresponding counterfactual output. For instance, given a function that checks for close floating-point elements in a list, the model is expected to reason whether the list and threshold would yield a different outcome if indexing conventions were altered. The full example is shown as follows:

HumanEval-Exe Causality & Causal Graph.

Open-Critic Example This dataset performs a programming-related task: code generation. It is a synthetic code editing benchmark designed to train and evaluate large language models on their ability to identify and fix bugs in code. Each example consists of a natural language task description, a correct code solution that satisfies the task, and a counterfactual explanation that describes bugs introduced into a similar but faulty implementation. The objective is for the model to learn to generate or repair the bugged version of the code based on the bug descriptions as a counterfactual scenario. For instance, given a correct nested loop that prints all valid digit combinations excluding the number 5 and repetitions, the counterfactual explanation points out altered range values (e.g., using range(100) and range(1)), leading to an incomplete iteration logic. The full example is shown as follows:

Open-Critic Causality & Causal Graph.

Code-Preference Example This dataset performs a programming-related task: code summarization. It contains pairs of duplicate code examples, with the only difference being the bugged code example has the bugged code ’surgically transplanted in’ while the corrected code is left the same. Each example consists of a natural language instruction, a correct code solution that satisfies the instruction, and a bug explanation that describes bugs. The objective is for the model to learn to summarize and generate the bug descriptions as a counterfactual scenario. For instance, given a correct nested loop that prints all valid digit combinations excluding the number 5 and repetitions compared with a bugged loop, the bug description generated by the model will be able to point out altered range values (e.g., using range(100) and range(1)) leading to an incomplete iteration logic in its summarized response of bug explanation. The full example is shown as follows:

Code-Preferencec Causality & Causal Graph.

## Appendix B Complementary Experiment

This section presents additional experimental results that complement the main evaluation in the body of the paper. These supplementary findings, together with what has been presented in previous sections, offer comprehensive insights into LLMs capabilities across different decompositional tasks.

Figure: Figure 5: Additional evaluation on causal graph construction, complementing to Figure [3](https://arxiv.org/html/2505.11839v1#S4.F3).
Refer to caption: x5.png

Figure: Figure 6: Additional evaluation of LLMs’ intervention identification, complementing to Figure [4](https://arxiv.org/html/2505.11839v1#S4.F4).
Refer to caption: x6.png

## Appendix C Experimental Setting

This section lists the experimental settings used in this study.

**Table 6: LLM query hyperparameters used during all experiments.**
| Hyperparameter | Value | Description |
| --- | --- | --- |
| Temperature | 0.7 | Controls randomness in generation |
| Top-$p$ (nucleus sampling) | 0.95 | Probability mass for sampling |
| Max tokens | 2048 | Maximum number of tokens to generate |
| Stop sequences | ["\n", "Q:"] | Used to truncate responses |
| Prompt format | CoT, CoT-SC, ToT | Prompting strategy used in Section [4.2.2](https://arxiv.org/html/2505.11839v1#S4.SS2.SSS2) |
| Tool-calling API | Enabled (Selective) | Used in tool-augmented experiments |

Computational Resources. All experiments were conducted on a high-performance computing server equipped with six NVIDIA RTX 6000 Ada Generation GPUs, each with 49 GB of dedicated VRAM. The system utilized CUDA version 12.8 and NVIDIA driver version 570.124.06. These GPUs supported parallel execution of model querying, evaluation, and tool-augmented tasks across our benchmark datasets. The hardware configuration ensured sufficient memory bandwidth and processing capability to accommodate large-scale inference, particularly for multimodal tasks and multi-sample prompting strategies such as CoT-SC and ToT. No resource-related constraints were encountered during experimentation.

## Appendix D Discussion of Limitation

While our decompositional framework offers a structured view for evaluating LLMs’ counterfactual reasoning abilities, several limitations are worthy to consider as further steps.

##### Scope of Causal Structures.

Our work focuses on fundamental and representative causal structures involving four core variable types (Exposure, Covariate, Mediator, Outcome). Although this abstraction aligns with foundational causal inference principles, it may not fully capture all the possible granularity of real-world causal systems, which often involve latent confounders, feedback loops, or dynamic temporal interactions. Future extensions could incorporate more complex or hierarchical causal graphs to evaluate how LLMs perform under less idealized conditions.

##### Tool Dependency and Integration.

The proposed tool-augmented approach introduces external model dependencies (e.g., Grounding-DINO, GraphCodeBERT), which improve performance but add system complexity. These tools themselves are imperfect and may introduce noise or domain mismatches. Further investigation is needed to understand how errors from auxiliary tools propagate through the LLM pipeline and affect final reasoning outputs.

##### Benchmark Coverage.

Although our benchmark spans diverse domains—natural language, vision-language, code, and symbolic reasoning—it is still constrained by the selected datasets and intervention types. Other domains such as medical decision-making, real-time interaction, or policy planning remain unexplored. Expanding the benchmark to broader applications would provide more robust insights into LLM generalization in counterfactual reasoning.

## Peer Reviews

confidence: 3
contribution: 2
initial_rating: 4
presentation: 2
questions: See weaknesses.
soundness: 2
strengths: It has been observed repeatedly that LLMs perform worse when answering counterfactual queries relative to factual ones. The paper’s attempt at understanding why in a more fine-grained fashion is a significant problem.

Experimental evaluations are comprehensive in terms of the number of models and the variety of datasets they consider.
summary: The paper proposes a decomposition of counterfactual reasoning into smaller sub-tasks: causal variable identification, causal graph construction, counterfactual identification, and outcome reasoning. The authors evaluate the performance of different LLMs in performing these sub-tasks across a variety of datasets, identify variable identification and counterfactual identification as a weak point, and propose strategies to improve it.
weaknesses: It is not clear how the performance on the four sub-tasks relate to the end-to-end performance. Establishing this relation is important when interpreting performance on these sub-tasks as decomposition.

For instance, the paper concludes that LLMs are generally better at Task 1 than Task 2 conditioned on correct results from Task 1 (which is supported by experiments and indeed seems to be the case). However, it could still be the case that starting from the inputs of Task 1 and directly querying for the outputs of Task 2 is as easy as performing Task 2 conditioned on correct results from Task 1. If, for instance, processing X, Y, Z variables implicitly is somehow easier than outputting them explicitly.

While the above is probably unlikely to be the case, it is a crucial validation to perform in order to confirm that Task 1 and Task 2 (conditioned on Task 1 results) behave as intended. Evidence provided in the paper does not provide support for attributing poor end-to-end Task-1 inputs to Task-2 outputs performance to poor Task 1 performance. 

I want to emphasize that this is a general consideration for all sub-tasks, the entire chain, not just Tasks 1 and 2 specifically.

The paper doesn’t discuss the related work to a sufficient depth. In particular, other methods of evaluating counterfactual reasoning and whether there have been attempts at decomposing this process into smaller steps in evaluation is not discussed.

The four sub-tasks that are identified, while generally sound, are arbitrary. Their choice is not motivated.

While evaluations in Section 4.1 are interesting, they are not exactly actionable. They help identify which sub-tasks are the weak points to be improved (which arguably all a decomposition is supposed to do) and the paper suggests improvement for these weak points. However, the suggestions take the decomposition as a given. When the goal is to answer a question end-to-end, starting from the given of Task 1 and providing the outputs of Task 4, the paper does not explore the impact of going through Tasks 1, 2, 3, and 4 as a pipeline rather than direct queries. Moreover, it does not explore whether the task specific improvements in Section 4.2 lead to a comparable end-to-end gain as well.

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 3
questions: See weakness
soundness: 3
strengths: 1. The overall methodology of decomposing the counterfactual reasoning process is novel and the experiments show this really helps.
2. The experiments cover a wide range of dataset design specifically for counterfactual reasoning.
3. The final proposed method seems to be easy to adopt for any LLM for reasoning.
summary: This paper examines counterfactual reasoning in LLMs across a range of modalities and models.
The paper shows that by decomposing the reasoning scenario to causality construction and counterfactual intervention, the reasoning ability of LLMs under counterfactual scenarios can be greatly improved.
By leveraging tools that can help the model identify the causal variable, the performance can be further improved.
weaknesses: 1. The experiments covers many datasets, but it lacks comparison on model scale, for example, Qwen 3 provides models across different scales, it could make the paper stronger if some results are shown there.
2. The NER tools are designed to use Bert like models, however, would it be possible that the tools are instantiated by another model using different prompts?

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 3
questions: The benchmark relies on curated causal structures. To what extent do you believe the performance on these structured tasks generalizes to real-world scenarios where causal graphs are not provided and must be inferred from raw data? Did you run any experiments on "raw" data without this scaffolding?
soundness: 3
strengths: 1.  **Systematic and Granular Evaluation Framework:** The paper's primary strength is its decompositional approach, which breaks down the complex task of counterfactual reasoning into four distinct, measurable stages. This allows for a much more precise diagnosis of *where* and *why* LLMs fail, moving beyond a monolithic "pass/fail" assessment to identify specific bottlenecks, such as the particular difficulty with implicit mediators.

2.  **Comprehensive and Multimodal Benchmark:** The authors construct a substantial and diverse benchmark by curating 11 datasets spanning various modalities (text, image, code, symbols) and tasks. This breadth ensures that the findings are not limited to a single domain and provides a robust testbed for evaluating the generalizability of LLMs' counterfactual reasoning abilities.

3.  **Actionable Insights and Proposed Solutions:** The paper goes beyond mere identification of problems by proposing and evaluating concrete strategies for improvement. The demonstration that tool-augmentation can alleviate modality-specific issues and that advanced prompting can help with implicit reasoning provides valuable, actionable directions for future research aimed at enhancing LLM reasoning capabilities.
summary: This paper makes three key contributions:

- It introduces a **decompositional framework** for evaluating counterfactual reasoning in LLMs, breaking it into four stages: causal variable identification, causal graph construction, counterfactual intervention identification, and outcome reasoning.
- It constructs a **comprehensive multimodal benchmark** using 11 datasets across text, image, code, and symbolic modalities, enriched with causal annotations and graphs.
- It provides an **in-depth evaluation and improvement strategy**, identifying key bottlenecks (especially in mediator reasoning and cross-modal tasks) and proposing tool augmentation and advanced prompting techniques to enhance LLM performance.
weaknesses: I think there are 2 brief weaknesses of the paper:

*   **Potentially Artificial Evaluation:** The benchmark relies on pre-annotated causal structures, which may not reflect the challenge of inferring causality from raw, unstructured data.

*   **Surface-Level Diagnosis:** The analysis identifies performance bottlenecks but offers a high-level explanation (e.g., "working memory") without deeply investigating the underlying architectural mechanisms in LLMs that cause these failures.

---

confidence: 2
contribution: 3
initial_rating: 6
presentation: 2
questions: - Can you answer/address the above weaknesses?

- In the datasets, are the examples *only* made up of the $X,T,M,Y$ variables, or are any other variables present such that the model must extract the relevant variables *among other options*?

- Did humans or LLMs annotate the paper's benchmark dataset? Which of these scored LLMs' answers, or was this done through another method?
soundness: 3
strengths: - The approach is original; I have never seen any attempt at explaining the difficulties of LLMs in causal reasoning.

- The division into subtasks looks sound to me.

- Diversified source of tasks.
summary: Large language models typically struggle on counterfactual reasoning tasks, however it is unclear *why* they struggle, particularly at which parts of such reasoning. This submission proposes a benchmark to evaluate different aspects in LLMs' causal reasoning.They isolate that LLMs struggle to properly identify causal variables and find the counterfactual mediator and outcome even when given the variables, their relationships and the intervention to perform.
weaknesses: - The benchmark seems to impose the strict presence of four variables : covariates, treatment, mediator, outcome. However, the mediator could be absent from the context, as in the example:  “A person is running a marathon and collapses.” l.467-469. It is unclear why LLMs should explicitly,identify “Dehydration” as a mediator, and not other sources.

- It is unclear what prompts are given to LLMs : from my understanding, it is the "CRASS Prompt" given in Appendix A, but it seems to apply to all datasets?

- Typo? "the primary challenge lies not in" l.390-391 : it should be "lies in", right?


Decision: Accept (Poster)