Venue: NAACL 2025

# CausalEval: Towards Better Causal Reasoning in Language Models

Longxuan Yu\({}^{\dagger}\), Delin Chen\({}^{*}\)+, Siheng Xiong\({}^{++}\), Qingyang Wu\({}^{\lx@sectionsign}\),

Dawei Li\({}^{\heartsuit}\), Zhikai Chen\({}^{\clubsuit}\), Xiaoze Liu\({}^{\spadesuit}\), Liangming Pan\({}^{\diamondsuit}\)

\({}^{\dagger}\)UC San Diego \({}^{~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} ~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{} {}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~{}~

causal inferences or as the **helper** providing extra information to traditional CR methods (Figure 1). Despite significant progress, challenges such as the shallow depth of causal reasoning skills and the scarcity of high-quality data still require further exploration Zecevic et al. (2023). Addressing these challenges is crucial, as enhancing causal reasoning not only improves the general performance of LLMs but also provides substantial benefits for tackling complex reasoning problems and supporting decision-making Liu et al. (2024). Thus, integrating causal inference into the model architecture Melnychuk et al. (2022) or during the pre-training phase Rohekar et al. (2023) presents a promising direction for future research.

In this work, we focus on methodologies to enhance the causal reasoning capabilities of LLMs, exploring practical challenges and identifying potential opportunities. We introduce a fine-grained **taxonomy** (SS3) that systematically reviews and summarizes current techniques employed in LLMs to improve CR. To evaluate these capabilities, we conduct extensive **assessments** (SS4) of widely-used LLMs on existing benchmarks, providing key findings and detailed analysis. Additionally, we discuss the **limitations** of current approaches (SS5) and propose promising directions for **future research** (SS6). Previous work Liu et al. (2024); Wan et al. (2024) reviews causal reasoning in LLMs, focusing more on the perspective of _traditional causal reasoning_. In contrast, our focus lies in the _methodological advancements_ that enhance the causal reasoning abilities of LLMs. More importantly, we conduct _practical evaluations_ of LLMs on various CR tasks, offering valuable experimental results and insights that are often lacking in related literature. By bridging theoretical advancements and practical evaluations, our study fills a critical gap in the literature and offers insights to guide the development of LLMs with enhanced causal reasoning.

## 2 Preliminaries

CR in the context of machine learning (ML) involves understanding and modeling cause-and-effect relationships within data, which goes beyond the traditional ML focus on correlations. Traditional ML methods often identify patterns or correlations, limiting their applicability in understanding the mechanisms behind complex systems Peters et al. (2017). Causal ML, on the other hand, addresses this gap by focusing on cause-and-effect relationships, which is crucial for making informed decisions, predicting the effects of actions, and understanding complex systems Prosperi et al. (2020); Fernandez-Loria and Provost (2022); Lagemann et al. (2023); Berrevoets et al. (2023).

The CR framework can be categorized into three levels: association, intervention, and counterfactual Pearl et al. (2000); Pearl and Mackenzie (2018), which form "the ladder of causality" (Table 1). As

\begin{table}
\begin{tabular}{c c c} \hline \hline Levels & Equation & Query \\ \hline Association & \(P(y|x)\) & What is? \\ Intervention & \(P(y|\text{do}(x),z)\) & What if? \\ Counterfactual & \(P(y_{x}|x^{\prime},y^{\prime})\) & Why? \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Ladder of Causality

Figure 2: Structure overview. We categorize the role of LLMs in causal reasoning into two main functions: as reasoning engine and as helper. Each function is further divided into specific methodologies. We also outline the evaluation process, including tasks, benchmarks, results and analysis.

 

sociation is formalized in statistical terms through conditional probability between variables. Intervention is represented using Pearl's do-operator to explore causal effects, while counterfactual reasoning involves hypothetical scenarios that address potential outcomes under different circumstances. Structural Causal Models (SCMs), often visualized with Directed Acyclic Graphs (DAGs) where nodes represent variables and edges indicate causal relationships [14], are widely used to represent the interplay of these components. SCMs provide a mathematical framework for formalizing causal relationships within a system, thus serving as the foundation for causal reasoning and causal learning [23]. More details about CR tasks, and the comparison between LLMs and traditional methods, are provided in Appendix A.

## 3 Towards Causal Reasoning in Large Language Models

We separate the roles of language models in CR into two categories. First, LLMs can serve as **causal reasoning engines**, employing methods such as fine-tuning, prompt engineering, external tool integration, and alternative approaches to directly draw causal inferences from input prompts. Second, LLMs can act as **helpers** to traditional methods by extracting causal information and generating causality data to enhance causal analysis across various domains.

### Serving as Causal Reasoning Engines

#### 3.1.1 Injecting Causal Knowledge through Fine-Tuning

Fine-tuning language models has been proven to be a simple and effective technique for transferring learned knowledge from a pre-trained model to new downstream tasks. Compared to training from scratch, it can significantly reduce training time, and improve performance on new tasks [9]. For example, Li et al. (2021) used self-supervised learning strategies, such as causal pairs ranking and fine-tuning, to inject causal knowledge into pre-trained models. Additionally, Zheng et al. (2023) proposed Causal Effect Tuning, which is based on causal inference, to learn new knowledge from target data while preserving old knowledge from pre-trained models, thereby building a causal path without storing the pre-training data.

Although scaling language models increases the effectiveness of their prior knowledge in addressing complex causal questions, most training corpora collected from online sources consist of unstructured textual data rather than structured causal annotations. To address this, Jiang et al. (2023) collected a custom dataset containing causal questions and interpreted intents in a structured format for supervised fine-tuning. Building on this, Chen et al. (2024) combined the structural causal model with instruction tuning [8, 56, 7] to develop a meta Structural Causal Model, which unified different NLP tasks under a single causal structure and introduced the Structural Instruction Tuning method to learn task-specific causal representations that mimic the causal factors for each task.

LimitationsDespite these progresses, LLMs often lack clearly defined supervision for causality, as most ML datasets contain only observational data without interventions, making key causal quantities, such as treatment effects, unknown [12]. Furthermore, causality datasets often feature complex, heterogeneous relationships between variables [11], which are

Figure 3: Overview of methods for LLMs as causal reasoning engines. (a) Fine-Tuning: Adapting LLMs using large-scale causal-effect pairs and target datasets. (b) Prompt Engineering: Crafting targeted prompts to elicit the internal CR capabilities. (c) Tool Integration: Leveraging external tools to support LLMs in performing intermediate steps. (d) Alternative Approaches: Implementing additional methods, such as iterative improvement protocols, multi-agent systems, and rationale-based evaluation.

 

more challenging to capture compared to linguistic or perceptual patterns. Thus, developing methods to bridge this gap, such as integrating causal structure learning or leveraging synthetic data for interventions, remains an important area of research.

#### 3.1.2 Embedding Causal Knowledge within Contexts

Prompt-based methods have become crucial in unlocking the internal reasoning capabilities of LLMs across various tasks. Compared with general reasoning, CR tasks, particularly multi-step ones, pose greater challenges due to factors such as the complexity of understanding cause-and-effect relationships, the need for reasoning about interventions and counterfactuals, and the presence of confounding variables Gandhi et al. (2023); Tan et al. (2023); Ban et al. (2023); Abdali et al. (2023).

To address these challenges, various innovative prompting strategies have been proposed Liu et al. (2023b); Bagheri et al. (2024). Building on Chain-of-Thought (CoT) prompting Wei et al. (2023), CausalCoT Jin et al. (2023a) enables LLMs to reason step-by-step through causal relationships, significantly improving causal inference tasks involving multiple causes and effects. Recent advancements, such as Causal Contextualized Learning CausalCL Wang et al. (2023b), and Zhang et al. (2023) further enhances CR by combining code-like prompting with chain-of-thought prompting, boosting the performance for multihop event reasoning. Multi-modal Causal Reasoning (MCR) Zang et al. (2023) extends CR to multi-modal settings, integrating visual and textual information for a comprehensive analysis. Counterfactual Prompting Learning (CPL) He et al. (2022) utilizes counterfactual thinking to guide models in exploring alternative scenarios, thereby improving their understanding of causal mechanisms.

These advanced prompting techniques collectively enhance the ability of LLMs to understand causal relationships, thereby improving precision in CR tasks and enabling models to manage long-term dependencies more effectively Lu et al. (2022). Furthermore, they reduce biases from training data, enrich the model's knowledge with external information and commonsense, and do so without substantial increases in computational costs Yao et al. (2022); Jin et al. (2023a).

LimitationsCausal prompt engineering depends on the model's inherent causality knowledge, which is often insufficient due to the correlation-driven nature of its modeling approach Zhang et al. (2024). Crafting effective prompts is labor-intensive, requiring significant domain expertise. Furthermore, the sensitivity to prompt variations can lead to inconsistent outputs, reducing robustness Perez et al. (2021). Additionally, multi-step causal prompts can increase computational inefficiency, demanding more resources and time. These limitations highlight the necessity of complementary approaches, such as explicit causal modeling or specialized fine-tuning.

#### 3.1.3 External Causal Tool Integration

The motivation for integrating external tools into LLMs is that effective causal reasoning requires more than just internal model representations. By accessing structured knowledge bases and performing specialized computations, external tools enhance LLMs' capacity for complex causal reasoning, complementing their inherent capabilities. The ability to use these tools can be acquired through methods such as fine-tuning, in-context learning, and prompt manipulation. This integration establishes a crucial link between causal reasoning and language understanding, enabling LLMs to handle complex causal tasks more effectively.

For methods that incorporate tools in prompts, PLAN Lu et al. (2023) introduced a causal framework for procedural planning that leverages external knowledge bases, such as ConceptNet Speer et al. (2017). It semantically parses tasks into an entity set to retrieve relevant sub-graphs for improved planning. Additionally, Pawlowski et al. (2023) compared context augmentation and tool augmentation methods: context augmentation uses language models for supplementary operations beyond the causal expert system, whereas tool augmentation incorporates basic Python tools to manipulate outputs from the expert system. Their experiments showed that context-augmented LLMs were more prone to errors compared to tool-augmented ones. For methods involving supervised fine-tuning, dialogue agents such as those by Shuster et al. (2022); Komeili (2021) are trained to effectively use search engines.

LimitationsWhile integrating external causal tools can enhance LLMs, it also introduces several challenges. The effectiveness of these tools depends heavily on the quality and completeness of external resources, which can lead to errors when 

inadequate. The alignment between the model and tools is often complex and domain-specific, limiting the model's generalizability. Additionally, reliance on external tools may slow response times and increase the risk of errors, as observed with context-augmented models (Pawlowski et al., 2023). These challenges indicate that external tools should be used in combination with other approaches to fully address complex CR tasks.

#### 3.1.4 Alternative Approaches

Recent studies have explored various alternative approaches to enhance the causal reasoning capabilities of LLMs, including iterative improvement protocols, multi-agent systems, and rationale-based evaluation. For example, ALLURE (Hasanbeig et al., 2023) uses a systematic auditing and _iterative improvement protocol_ with in-context learning examples to refine LLM evaluation. ECHO (Xie et al., 2023) introduces a visio-linguistic dataset based on real-world scenarios to evaluate event causality through human-centric reasoning.

_Agentic frameworks_ have also been employed for causal reasoning in LLMs. CausalGPT (Tang et al., 2023) employs a multi-agent system comprising reasoners and evaluators, with reasoners generating answers and evaluators assessing factual accuracy and causal consistency. Du et al. (2023) presents a debate framework where multiple instances of LLMs debate a causality-related query to reach a consensus. _Selection and inference-based techniques_ are another approach. Creswell et al. (2022) alternate between selection and inference steps using pre-trained LLMs to generate interpretable causal reasoning steps, while Creswell and Shanahan (2022) chain reasoning steps to produce valid reasoning traces. _Rationale-based evaluation methods_ are also receiving increased attention. Atanasova et al. (2023) use rationale-based methods to conduct tests for evaluating the faithfulness of natural language explanations. Abdali et al. (2023) generate a prior model to address domain knowledge gaps, serving as a heuristic for assessing the informativeness of user feedback. Additionally, Yue et al. (2023) utilize a cascaded model incorporating CoT and Program-of-Thought (PoT) approaches, along with answer sampling and consistency checking, to enhance causal reasoning.

### Enhancing Traditional CR Methods

Beyond serving as causal reasoning engines, LLMs also act as helpers in causal analysis across domains (Figure 4), fulfilling several supportive roles: (1) _Causal Information Extraction_: LLMs can extract causal variables, event sequences, and meta-knowledge from unstructured text, enhancing the quality of causal discovery in real-world applications (Yang et al., 2023; Abdali et al., 2023; Li et al., 2024). Additionally, by leveraging prior knowledge, they identify and adjust for biases and confounders, improving the robustness of causal inferences (Ban et al., 2023). (2) _Causality Data Generation & Augmentation_: LLMs generate synthetic, causally relevant data, such as cause-effect pairs, counterfactuals, and causal subgraphs, thereby enriching existing datasets and supporting traditional CR methods (Stalunaite et al., 2021). Furthermore, they form causal hypotheses from observed data patterns, generate plausible counterfactuals, and reason through complex interventions, guiding CR methods to explore less obvious causal pathways and enabling effective outcome evaluation (Gat et al., 2023). These supportive roles illustrate the potential of LLMs to complement traditional causal analysis methods by bridging gaps in data, knowledge, and reasoning. Through their ability to extract hidden causal information and generate synthetic data, LLMs enable more robust and comprehensive causal reasoning. More details on these supportive roles are provided in Appendix B.

## 4 Evaluating Casual Reasoning in Large Language Models

### Experimental Setup

We first categorize the end tasks into three groups: causal discovery, causal inference, and additional causal tasks. For each category, we evaluate recent LLMs with strategies such as zero-shot, few-shot, direct I/O prompting, and Chain-of-Thought (CoT) reasoning. To reduce variance, we evaluate

Figure 4: Overview of methods for using LLMs to enhance traditional approaches. (a) Information Extraction: Extracting causal variables and events from text and adjusting for biases. (b) Data Generation: Generating synthetic causal data and forming hypotheses.

 

each question 5 times using greedy decoding and compute the average accuracy (pass@1) for that question. We then calculate the overall average accuracy across all problems. Specifically, we use COPA Roemmele et al. (2011), NPDS Tu et al. (2019), e-CARE Du et al. (2022), and Corr2Cause Jin et al. (2023b) for causal discovery; CLADDER Jin et al. (2023a) and CRAB Romanou et al. (2023) for causal inference; and CRASS Frohberg and Binder (2022), MoCa Nie et al. (2023), and Tram Wang and Zhao (2023) for additional causal tasks. Further details on task definitions, dataset statistics and examples, and implementation details are provided in Appendix C and Appendix D.

### Main Results

Overall performance is shown in Table 2. We summarize the key observations as follows:

**LLMs significantly underperform humans in many CR Tasks.** Our results reveal a notable performance gap between LLMs and humans across many causal reasoning tasks, as illustrated in the Radar chart Figure 8, Appendix D). Despite recent advancements, LLMs continue to face challenges in fully grasping and applying causal reasoning, often relying on surface-level patterns and struggling with multi-step reasoning. These limitations

\begin{table}
\begin{tabular}{l|c c c c|c c c|c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c|}{**Coansal Discovery**} & \multicolumn{4}{c|}{**Coansal Inference**} & \multicolumn{4}{c}{**Additional Causal Tasks**} \\ \cline{2-16}  & **COPA** & **NPDS** & **e-CARE** & **Corr2Cause** & **CLADER** & **CRAB** & **CRASS** & **MoCa** & **Tram** \\ \hline Mistral-7B & 86.2  & 63.4  & 50.6  & 35.6  & 55.6  & 75.6  & 75.8  \\ + CoT & 88.8  & 65.0  & 36.6  & 36.6  & 36.6  & 36.7  & 55.6  & 75.6  & 35.4  & 75.8  \\ + CoT & 88.8  & 75.2  & 77.8  & 73.0  & 76.8  \\ + CoT & 82.8  & 65.0  & 65.0  & 36.6  & 36.6  & 43.6  & 66.7  & 71.7  & 53.5  & 76.8  \\ Gemma-2.9B & 91.2  & 67.0  & 69.4  & 31.0  & 56.1  & 43.5  & 41.7  & 57.6  & 75.1  & 77.6  \\ + CoT & 96.0  & 77.0  & 80.6  & 33.6  & 23.8  & 57.6  & 44.7  & 86.7  & 57.6  & 54.9  & 76.7  & 78.6  & 11.6  & 76.8  & 11.6  & 77.6  & 78.6  & 11.6  & 79.6  & 78.6  & 11.6  & 83.1  & 48.6  & 76.7  & 58.3  & 84.3  & 83.2  & 84.1  & 86.8  & 76.7  & 54.9  & 76.6  & 78.6  & 11.6  & 83.1  & **42.4 ** & 57.6  & 44.7  & 86.7  & 57.6  & 54.9  & 76.8  & 11.6  & 83.1  & **42.1 ** & 57.6  & **44.7 ** & 86.7  & 54.9 [FOOTNOTE:1]Footnote 1: foot 

reduce their effectiveness in complex scenarios. Future work should focus on enhancing LLMs' ability to handle intricate causal structures, potentially through hybrid methods that integrate symbolic reasoning with statistical approaches. Addressing these challenges will be critical for bridging the gap between LLMs and human performance in causal reasoning.

**Performance disparity exists between discovery and inference tasks due to contextual and multi-variable reasoning demands.** Our results reveal distinct performance trends across causal tasks. All models achieve high accuracy in certain discovery tasks, such as COPA and e-CARE, which require identifying potential causal relationships from observational data. However, their performance drops significantly in inference tasks, particularly when assessing causal strengths from long contexts (CRAB). Discovery tasks involving complex multi-variable interactions, such as Corr2Cause, also present significant challenges. This disparity underscores a fundamental limitation: while models are proficient at identifying potential causal relation candidates, they struggle with evaluating causal strengths and navigating intricate variable dependencies.

* performance on benchmarks such as COPA, NPDS, and e-CARE remains comparable to or worse than that of standard models. This trend indicates that deep thinking approaches primarily enhance structured causal reasoning rather than general causal understanding.

### Analysis and Discussion

We further analyze the impact of various factors on accuracy below.

**Zero-shot or few-shot under CoT prompting.** Figure 5 shows that both zero-shot and few-shot models benefit from CoT prompting, with few-shot

\begin{table}
\begin{tabular}{l|c c c c|c c|c c c} \hline  & \multicolumn{4}{c|}{**Coausal Discovery**} & \multicolumn{2}{c|}{**Cousal Inference**} & \multicolumn{2}{c}{**Additional Causal Tasks**} \\ \cline{2-10}
**Method** & **COPA** & **NPDS** & **e-CARE** & **Corr2Cause** & **CLADDER** & **CRAB** & **CRAS** & **MoCa** & **Term** \\ \hline GPT-3.5-turbo & 89.8 & 54.0 & 72.0 & 41.8 & 52.4 & 32.0 & 75.0 & 56.2 & 76.6 \\ + CoCT & 95.2 & 70.0 & 82.8 & 34.6 & 54.8 & 32.6 & 85.0 & 56.9 & 82.4 \\ + causalityoCT\({}^{*}\) & 49.0 & 28.0 & 37.0 & 35.0 & 45.0 & 19.0 & 52.0 & 45.0 & 48.0 \\ + FunctionCall\({}^{*}\) & 78.0 & 58.0 & 63.0 & 35.0 & 57.0 & **44.0** & 68.3 & 53.0 & 75.0 \\ \hline GPT-4o\({}^{*}\) & **100.0** & 56.0 & 85.0 & 47.0 & 61.0 & 33.0 & **95.0** & **61.0** & 84.0 \\ + CoT\({}^{*}\) & 99.0 & **73.0** & **90.0** & **49.0** & 69.0 & 42.0 & **100.0** & **61.0** & **87.0** \\ + causalCoT\({}^{*}\) & 94.0 & 58.0 & 75.0 & 47.0 & 75.0 & 36.0 & 93.0 & 56.0 & 78.0 \\ + FunctionCall\({}^{*}\) & **100.0** & **73.0** & 73.0 & **49.0** & **76.0** & 42.0 & 98.3 & **61.0** & 84.0 \\ \hline Human & 95.8 & 97.7 & 92.0 & 94.5 & 94.8 & 98.7 & 98.2 & 92.0 & 98.8 \\ \hline \end{tabular}
\end{table}
Table 3: Performance comparison between Direct IO, CoT, casualCOT prompting and functionCall (Python script) on GPT-3.5-turbo and GPT-4o across various causal reasoning datasets. Performance is measured using pass@1 accuracy, with the best results in each category in bold. \({}^{*}\)Model is evaluated on 100 random samples due to cost and time constraints.

Figure 7: Error analysis across different types of causal reasoning tasks. For each task type, multiple datasets are analyzed with 100 randomly selected incorrect test samples. The y-axis shows the number of incorrect samples per error type: LE (Logical Errors), PE (Probabilistic Errors), SE (Statistical Errors), and CE (Contextual Errors). Detailed definition of each error type is given in Appendix D.

 

CoT achieving the highest overall performance. While few-shot consistently outperforms zero-shot across all tasks, the models struggle the most with inference tasks, where accuracy remains lower than in discovery and additional tasks. These findings highlight the effectiveness of combining example-based learning with structured reasoning but also underscore the need for further improvements, especially in inference tasks, to enhance model performance across complex causal scenarios.

**Model size vs. Causal reasoning performance.** Figure 6 demonstrates that causal reasoning performance improves consistently with increasing model size across all evaluated models. Both LLaMA3 and LLaMA3.1 achieve significant gains, with Gamma also exhibiting strong improvements. In contrast, Mistral shows only modest improvements as model size increases, likely due to its Mixture of Experts (MoE) architecture. These results underscore the importance of both model size and architecture in advancing causal reasoning capabilities.

**Error analysis.** Through systematic analysis of model outputs, we identify four common types of errors in causal reasoning.

_Logical errors_ occur when the model makes flawed cause-effect conclusions, such as confusing correlation with causation or failing to distinguish between necessary and sufficient conditions. For example, when asked whether someone caused a car to start in a scenario with multiple people each bringing a working battery, the model incorrectly attributes causation based solely on temporal sequence rather than considering the true causal relationship.

_Probabilistic errors_ arise when the model incorrectly handles numerical reasoning about likelihood and chance, including mishandling of conditional probabilities and confounding variables. In probability comparison tasks, the model often makes computational errors or misinterprets statistical dependencies, leading to incorrect conclusions about relative likelihoods.

_Statistical errors_ manifest when the model fails to properly account for complex relationships between multiple factors, such as indirect effects and selection bias. For instance, in scenarios involving multiple causal paths, the model often overlooks intermediate factors or assumes direct relationships where more complex dependencies exist.

_Contextual errors_ occur when the model fails to incorporate relevant background information or subtle details that should affect its causal reasoning. This often results in oversimplified conclusions that ignore important contextual factors or conditions that would alter the causal relationship.

Our analysis (Figure 7) shows that logical errors are the most prevalent type of failure. Potential approaches to address these challenges include enhanced representation of causal relationships in the model architecture, systematic consistency checks for reasoning processes, and diversified training data covering a broader range of causal scenarios. Detailed examples of each error type and analysis of these approaches are provided in Appendix D.

**Comparison with enhancement methods.** We evaluated two methods for improving causal reasoning: CausalCoT [29] and Function Call. CausalCoT provides structured steps for causal inference with domain-specific guidance, while Function Call leverages Python scripts for precise mathematical computations. Our results show that effectiveness varies by task type. For formal reasoning tasks involving causal graph analysis, CausalCoT outperforms other approaches due to its structured inference steps (\(75.0\%\) on CLADDER). In contrast, Function Call excels in numerical tasks, particularly on CLADDER (GPT-4o: \(76.0\%\) vs \(69.0\%\) with vanilla CoT), and performs comparably well on other computation-heavy datasets. However, for commonsense causal reasoning tasks like COPA and e-CARE, both methods underperform: CausalCoT shows substantial drops (\(40.8\%\) and \(35\%\) respectively for GPT-3.5-turbo), while Function Call provides minimal gains (COPA: \(78.0\%\) vs \(95.2\%\) with vanilla CoT), suggesting that vanilla CoT remains the most effective for these tasks. These findings highlight the importance of task-aware method selection: Function Call for quantitative analysis, CausalCoT for structured causal inference with graphs, and vanilla CoT for commonsense causal reasoning.

## 5 Findings and Reflections

**LLMs demonstrate shallow causal reasoning skills.** Evaluation metrics often emphasize task-specific accuracy, leaving open questions about the depth of the model's causal reasoning [23]. For instance, Hong et al. hong23 investigated how LLMs process event B in a script-based story, which causally depends on a prior event A. While large models performed better than smaller ones in the \(\neg{\rm A}\rightarrow{\rm B}\) condition, all models 

(even fine-tuned with code and instructions) failed to replicate human behavior when the cause was omitted (nil), indicating that LLMs lack sufficient representation of commonsense CR.

**Integration/Collaboration enhances the performance.** Tasks that challenge a single LLM can often be addressed by integration with traditional methods, external tools, or multiple LLMs Tang et al. (2023); Du et al. (2023). Recent approaches, such as neuro-symbolic methods Yang et al. (2023) and iterative processes Ban et al. (2023), integrate LLMs with statistical analysis and causal structure learning, improving the quality of causal inferences. The integration with causal analysis tools increases LLMs' access to causal knowledge across domains Kiciman et al. (2023b). As intermediaries between specialized tools and natural language, LLMs make causal reasoning more intuitive and user-friendly. This collaboration fosters tools for comprehensively exploring causal relationships in complex systems.

**Diverse and standardized causal reasoning benchmarks are needed.** Comprehensive evaluation of the causal reasoning abilities of LLMs requires more diverse and standardized benchmarks. Existing benchmarks tend to be relatively simple and domain-specific. Future benchmarks should encompass a broader spectrum of causal reasoning challenges, including multi-step tasks with increasing complexity, while maintaining consistent causal structures. Such datasets would enable LLMs to explore a wider range of causal reasoning scenarios and better adapt to real-world applications Yang et al. (2024).

## 6 Future Directions

**Deep understanding of existing theories.** Integrating causal reasoning capabilities into LLMs presents unique challenges, especially when requiring adherence to established theories. These theories, such as the potential outcomes framework, graph-based causal inference methods, and structural equation modeling, often rely on foundational assumptions, including the stable unit treatment value assumption, ignorability/unconfoundedness, and positivity Liu et al. (2024b). Considering these assumptions necessitates innovative strategies, such as incorporating specific post-training data and implementing filtering mechanisms to ensure the model's alignment with the theories.

**Data efficiency for end-to-end learning.** LLMs have the potential to perform end-to-end causal reasoning, but their effectiveness depends on substantial data to learn and infer causal relationships. A major challenge is the scarcity of high-quality causality data, particularly for counterfactual reasoning, where alternative scenarios are often unavailable or difficult to generate. Improving data efficiency is therefore a critical research direction. Techniques such as analogical reasoning, imaginative simulation, and inferential methods can maximize the utility of existing data by generating synthetic datasets, filling data gaps, or extrapolating from limited information. These approaches enhance the model's capacity to learn causal relationships with constrained training data.

**Causality-based internal mechanisms for LLMs.** Incorporating causal reasoning into LLMs internally is supposed to enhance their accuracy and depth in complex problem-solving scenarios Kiciman et al. (2023b). Thus, it is crucial to find feasible implementation ways so that LLMs generate content grounded in causality. One strategy could be the introduction of a dual-network architecture, where one is similar to current model and the other focuses on learning causal relations. Both networks are jointly trained to perform various tasks. Another approach involves modifying attention mechanisms to prioritize causal relevance. By embedding internal causal mechanisms, we believe LLMs can enhance their reasoning, address fairness and safety, and better handle multi-modal inputs through improved understanding of causal relations, leading to more accurate and transparent decisions and reduced biases.

## 7 Conclusion

LLMs equipped with these advanced technologies demonstrate strong potential in causal reasoning, but further research is needed to fully realize and refine their capabilities. Addressing challenges such as managing intricate causal structures is essential for continued development. Enhancing interpretability and transparency through causal reasoning is crucial for building trust and helping users rely on model inferences. Exploring innovative methods will be key to overcoming current limitations and unlocking the full potential of LLMs in complex, real-world scenarios. Advancing these areas will make LLMs more reliable and effective tools for causal analysis across domains 



### Limitations

In this survey, we review advancements in causal reasoning with LLMs, though several limitations should be noted. Our focus is primarily on text-based causal reasoning, with limited exploration of multi-modal or visual tasks, which restricts the generalizability of our findings across modalities. Although we evaluate mainstream LLMs and methods on popular benchmarks, we do not cover all enhancement techniques, particularly those requiring post-training or tailored to specific tasks and domains. Additionally, understanding the fundamental principles of causal reasoning in LLMs is crucial for ensuring trust and accountability in real-world applications, yet this area remains challenging and underexplored. Further research is warranted to address these gaps.

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Causal Reasoning Tasks

Causal discovery and inference (Glymour et al., 2019; Pearl et al., 2000; Spirtes et al., 2000; Spirtes and Zhang, 2016; Pearl et al., 2016; Perry et al., 2022; Yao et al., 2021) are two related fields within various domains such as statistics and machine learning that aim to understand the relationships between variables in a system and identify cause-and-effect connections. _(1) Causal discovery_(Glymour et al., 2019; Zhu et al., 2019; Heckerman et al., 2006; Huang et al., 2018) aims to identify causal relationships from data, especially when the causal structure is unclear. It involves constructing models, such as causal graphs, to elucidate cause-and-effect. This is crucial as real-world constraints often make controlled experiments to establish causality impractical, unethical, or impossible (Spirtes et al., 2000; Vemuri, 2015; Peters et al., 2017). _(2) Causal inference_(Pearl, 2009; Yao et al., 2021; Shams and Beierholm, 2022; Pearl et al., 2016; Mooij et al., 2016; Kuipers and Moffa, 2022; Peters et al., 2017) involves adding data or interventions to a causal graph from causal discovery to test variable cause-effect relationships. It aims to determine the direct impact of a variable or intervention on another. The methods used for causal discovery may include constraint-based algorithms (Tsagris et al., 2018; Cooper, 1997; Chaudhry et al., 2018), score-based algorithms(Huang et al., 2018; Peters and Buhlmann, 2014; Chickering, 2002; Heckerman et al., 2006), hybrid algorithms (Tsamardinos et al., 2006) and so on (Zanga et al., 2022; Glymour et al., 2019). Constraint-based algorithms reconstruct causal graphs using conditional independence statements from statistical tests. Score-based methods optimize the fitness of the graph using score metrics from observed data. Hybrid approaches integrate both to enhance causal inference.

Although there has been much research based on traditional methods, they have several limitations in data requirements, model scalability, and model robustness that LLMs can address more effectively. _(1) Data Quality:_**Traditional Methods** require well-developed, high-quality datasets, making them vulnerable to missing data and measurement errors. They rely on assumptions like linearity, stationarity, and causal sufficiency, which may not hold in real-world scenarios. **LLMs** capture non-linear interactions and are suitable for various types of data. They can augment sparse or incomplete data through in-context learning, generating plausible hypotheses, and filling gaps. _(2) Scalability:_**Traditional Methods** can be computationally intensive and less scalable with increasing variables, often requiring costly intervention data. **LLMs** efficiently process large datasets and automate causal discovery, making them more scalable and adaptable to complex datasets. _(3) Robustness:_**Traditional Methods** simulate counterfactuals and predict intervention effects without explicit data, but their reliance on strict assumptions can limit robustness in practical applications. **LLMs** apply methods such as fine-tuning to dynamically integrate vast background knowledge, providing a richer context for causal reasoning and making them more robust to data quality issues.

## Appendix B Enhancing Traditional CR Methods

### Causal Information Extraction

LLMs can directly support causal reasoning by serving as knowledge bases or extracting causal information. The effectiveness of problem-solving algorithms can be improved by adopting a modular approach that separates the logic and control components (Kowalski, 1979). The logic component represents the essential knowledge for solving the problem, while the control component manages the problem-solving strategy. Decoupling these components enhances flexibility, allowing updates to the control strategy without altering the underlying knowledge base.

Yang et al. (2023) proposed a neuro-symbolic approach with two components: an LLM-based symbolic representation generator that translates natural language into formal knowledge for symbolic inference, and an LLM-free symbolic inference engine. Similarly, Abdali et al. (2023) leveraged LLMs' reasoning capabilities to extract causal variables and event sequences, providing a heuristic for assessing feedback informativeness in the absence of domain knowledge. Additionally, RealITCD (Li et al., 2024) introduced LLM-guided meta-initialization to extract meta-knowledge from textual information, improving the discovery process in real industrial settings.

Another notable contribution is the iterative LLM-supervised causal structure learning (ILSCL) framework introduced by Ban et al. (2023), which iteratively refines causal Directed Acyclic Graphs through feedback from LLMs. This framework highlights the efficiency and robustness 

of LLMs in improving causal discovery. More broadly, the ability of LLMs to extract causal information can reduce the dependency on purely data-driven methods and unlock new opportunities for hybrid CR approaches. As LLMs evolve, they are likely to play an increasingly important role in causal discovery and inference by connecting unstructured text with structured causal models, making CR more robust and adaptable across domains.

### Causality Data Generation & Augmentation

LLMs store a vast amount of knowledge within their weights, and several works have explored how to extract causal-related knowledge to support traditional causal reasoning models. For instance, Li et al. (2020) introduced a method where, given an input cause sentence, a causal subgraph is retrieved from an event causality network, and the most probable effect event is selected to guide the generation of an effect sentence. As LLMs scale, their capacity to generate causal alternatives has improved. For example, Staliunaite et al. (2021) used GPT-2 to generate alternatives and re-write filtered augmentation data for causal relations. Similarly, Gat et al. (2023) showcased how LLMs can generate counterfactual approximations efficiently, focusing on model-agnostic explanations that enhance the interpretability and faithfulness of CR methods.

However, despite the impressive knowledge stored within LLMs, they remain prone to hallucinations, often generating incorrect or misleading facts Wang et al. (2023a). This issue is particularly problematic in causal reasoning, where precision and factual correctness are essential for drawing valid inferences and supporting decision-making processes. Hallucinated outputs can lead to incorrect causal links, undermining the reliability of LLM-generated causal insights. Moreover, the lack of transparency in how LLMs generate such outputs further complicates efforts to identify and correct errors, posing challenges to researchers and practitioners alike.

Addressing these challenges requires strategies to ensure more controllable and precise causal data generation. High-quality causal data is essential not only for improving LLM accuracy but also for fostering trust in their outputs, particularly in real-world applications like healthcare, economics, and public policy. Future research should focus on robust methods to mitigate hallucinations and enhance the controllability of LLM-generated causal information, bridging the gap between language model capabilities and practical causality needs.

## Appendix C Tasks & Benchmarks Overview

We first categorize the various end tasks into three groups: causal discovery, causal inference, and additional causal tasks. Causal discovery involves identifying potential causal relationships between variables within large, often unannotated, datasets. This is crucial for understanding causal dynamics in complex systems. Causal inference refers to quantifying the impact of a treatment (or intervention) on an outcome, determining how changes in one variable causally influence another, typically using observational data. Beyond these two primary tasks, there are additional causal tasks such as causal attribution, counterfactual reasoning, and causal explanation. These tasks extend the scope of causal analysis, often integrating causal reasoning with other reasoning tasks.

We show the statistics of existing benchmarks for causal reasoning tasks (Table 4) with those used in our experiments in bold. We also provide their inclusion of the three levels of causality: association, intervention, and counterfactual. From this data, we draw several key insights: (1) _Opportunity for Improvement_: There is a need for more datasets incorporating **intervention** and **counterfactual** reasoning to comprehensively evaluate causal reasoning. (2) _Balanced Data_: Greater emphasis on **real-world** scenarios and underrepresented domains, such as health and social contexts, would enhance the robustness of causal benchmarks. (3) _Scaling Challenges_: Although large datasets exist, developing datasets for complex causal tasks such as interventions remains difficult, limiting their representation. (4) _Future Work_: Advancing causal reasoning in LLMs will require benchmarks that balance real and synthetic data, cover all three levels of causality, and span diverse domains. In addition, we show examples from each dataset used in our experiments in Figure 9.

## Appendix D Implementation Details

We begin by discussing the evaluation metrics used across different benchmarks. TRAM uses accuracy to assess causal reasoning tasks. NPDS evaluates performance with both F1 score and causal accuracy, while Corr2Cause employs F1 as the primary metric, alongside precision, recall, and accuracy. CRAB focuses on Pairwise and Graded 

Causality tasks, using Macro F1 for its 4-class Pairwise Causality Multiple Choice Question (MCQ) task. MOCA compares LLM responses with human participants using agreement (Agg), Area Under the Curve (AUC), Mean Absolute Error (MAE), and Cross-Entropy (CE), though we directly assess LLMs on causal tasks without relying on agreement metrics. CRASS uses top-1 accuracy, while e-CARE assesses MCQs with accuracy, average-BLEU (n=4), ROUGE-L, Perplexity, and Causal Explanation Quality (CEQ) for explanations. Finally, COPA uses accuracy as the primary metric.

Across these benchmarks, metrics such as F1, precision, recall, AUC, and BLEU assess different aspects of performance, including classification quality, alignment with human responses, and explanation quality. We select pass@1 accuracy as our primary metric because it is straightforward and widely used for evaluating multiple-choice and prediction-based causal tasks.

For evaluation, we randomly select 500 samples from each dataset's test set using a random seed of 42, or the maximum available size if fewer samples exist. Due to cost and rate constraints, models like GPT-4o and Claude-3.5 are tested on 100 samples. We utilize a unified prompt pattern (Figure 9) comprising three parts: (1) **Identity Recognition**, such as "You are a highly intelligent question-answering bot with profound knowledge of causal inference"; (2) **Question-Context**, which describes the specific question to be addressed; and (3) **Answer Format Control**, which guides the model's response. The latter instructs the model to either provide a direct answer with "Give me the answer directly" for Direct I/O or to begin with reasoning or evidence, concluding with the final result marked by "*******" for CoT reasoning, ensuring the output is easy to parse. In the few-shot learning setup, we

\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Dataset** & **Size (Unit)** & **Domain** & **Source** & **Association** & **Intervention** & **Counterfactual** \\ \hline  & \multicolumn{6}{c}{Causality Discovery} \\ \hline SemEval Tasks 8 [20] & 1.3K (P) & Mixed & Real & ✓ & & \\
**COPA** [2011] & 1K (Q) & Dailylife & Real & ✓ & & \\ CEpairs [2016a] & 108 (P) & Mixed & Real & ✓ & & \\ CausalNet [2016b] & 62M (P) & Dailylife & Synthetic & ✓ & & \\ MultiRC [2018] & 9.8K (Q) & Mixed & Real & ✓ & & \\
**NPDS** [2019] & 770 (R) & Health & Synthetic & ✓ & & \\ TCR Benchmark [2019] & 25 (D) & News & Real & ✓ & & \\ SemiEval Tasks 5 [2020] & 20K (S) & Mixed & Real & ✓ & & ✓ \\ CausalBank [2020] & 314M (P) & Mixed & Synthetic & ✓ & & \\ GLUCOSE [2020] & 4.8K (S) & Mixed & Real & ✓ & & \\ CausalLM (2021) & 9.8K (S) & Ethnic \& Gender & Real & ✓ & & ✓ \\
**e-CARE** [2022] & 21K (Q) & Mixed & Real & ✓ & & ✓ \\ CausalQA [2022] & 1.1M (Q) & Mixed & Real & ✓ & & \\
**Corr2Cause** [2023b] & 200K (S) & Mixed & Synthetic & ✓ & & \\ \hline  & \multicolumn{6}{c}{Causal Inference} \\ \hline ROC Stories [2016] & 100K (S) & Mixed & Real & ✓ & & \\ CSR [2019] & 16K (S) & Mixed & Real & ✓ & ✓ & ✓ \\ ART [2019] & 20K (S) & Mixed & Real & ✓ & & \\ CausalQA [2022] & 1.1M (Q) & Mixed & Real & ✓ & & \\ COPES [2023c] & 1.3K (P) & Mixed & Real & ✓ & ✓ & \\
**CLADER** [2023a] & 10K (S) & Mixed & Synthetic & ✓ & ✓ & ✓ \\
**CRAB** [2023] & 2.7K (P) & Dailylife & Real & ✓ & & \\ \hline  & \multicolumn{6}{c}{Additional Causal Tasks} \\ \hline SWAG [2018] & 113K (Q) & Mixed & Real & ✓ & & \\ CommonsenyeQA [2018] & 12K (Q) & Mixed & Real & ✓ & & \\ ATOMIC [2019a] & 24K (S) & Mixed & Real & ✓ & & \\ Cosmos QA [2019] & 35K (Q) & Mixed & Real & ✓ & & ✓ \\ SocialIQA [2019b] & 38K (Q) & Social & Real & ✓ & & \\
**CRASS** [2022] & 275 (Q) & Mixed & Real & ✓ & & ✓ \\
**MoCa** [2023] & 144 (S) & Mixed & Real & ✓ & & \\
**Tram** [2023] & 1.2K (Q) & Dailylife & Real & ✓ & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Benchmarks for causal reasoning tasks. Datasets used in our experiments are in bold. Sizes are reported in various units: P (causal pairs), R (causal relations), S (samples), Q (questions), and D (documents). We also provide their inclusion of the three levels of causality: association, intervention, and counterfactual.

 

use two fixed examples for most datasets and one for the CRAB dataset, which has a longer context. Direct I/O prompts request direct answers, while CoT prompts involve generating CoT examples with GPT-4o (Figure 10), followed by manual validation for accuracy. Pass @1 Accuracy is calculated using a string parsing function to extract answers from outputs tagged with "Answer" or "####.." Any model failing to follow the specified format is treated as a failure case.

We evaluate a diverse set of language models, including Mistral AI's instructional models, Google's Gemma models, Meta's LLaMA 3 and 3.1 series, DeepSeek's V3 and R1 models, OpenAI's GPT-3.5-turbo, GPT-4o, o1 and o3-mini, and Anthropic's Claude-3.5, ensuring comprehensive coverage of various architectures and capabilities. For open-source models, we used the API provided by the Replicate and Together platform, while proprietary models were accessed through their respective company APIs. The specific model versions used in our experiments are listed below:

* mistralai/Mistral-7B-Instruct-v0.3
* mistralai/Mistral-8x7B-Instruct-v0.1
* google/gamma-2-9b-it
* google/gamma-2-27b-it
* meta-llama/Meta-Llama-3-8B-Instruct
* meta-llama/Meta-Llama-3-70B-Instruct-Turbo
* meta-llama/Meta-Llama-3.1-8B-Instruct
* meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
* deepseek-V3
* deepseek-R1
* gpt-3.5-turbo-0125
* gpt-4o-2024-08-06
* claude-3-5-sonnet-20240620
* o1-2024-12-17
* o3-mini-2025-01-31

To assess human performance on each dataset, we first reviewed the original papers for the datasets and found that some Du et al. (2022; Frohberg and Binder, 2022; Wang and Zhao, 2023) already provided human evaluation results. For the remaining datasets, we asked three graduate students to solve 100 randomly selected problems from each dataset. Prior to the evaluation, the participants underwent training to develop a strong foundation in causal reasoning. They then solved the problems independently, and their average accuracy is calculated as the final result.

For our error analysis, we first categorize errors into four primary types. **Statistical errors** (SE) arise from issues in data collection, measurement, or analysis, distorting relationships between variables. Sub-classes include: _confounding_, where an extraneous variable influences both independent and dependent variables, creating misleading associations; _selection bias_, where the sample is unrepresentative of the target population, leading to biased estimates; and _measurement error_, where inaccurate data collection results in flawed assessments. **Logical errors** (LE) involve flaws in reasoning, such as _directionality errors_ (misidentifying causal direction), _inconsistency_ (contradictory reasoning), and _over-generalization_ (drawing broad conclusions from specific cases without proper support). **Probabilistic errors** (PE) stem from incorrect handling of probabilities and statistical inferences, including _Bayesian inference errors_ (misapplication of priors or updates) and _probability comparison errors_ (misinterpreting conditional probabilities or neglecting base rates). Finally, **contextual errors** (CE) occur when subtle details, such as shifts in tone or minor actions, are overlooked, leading to flawed causal inferences. We select three representative models (LLaMA3-8B, LLaMA3-70B, and

Figure 8: Overview of human and LLM performance on causal reasoning tasks, with annotated human scores.

 

GPT-4o) for error analysis. For each dataset, 100 incorrect test samples are randomly chosen and manually analyzed. The results and analysis are presented in Section 4.3.

**Detailed Error Analysis** This appendix provides detailed examples and analysis of the error types discussed in Section 4.3. For each type, we present representative cases that illustrate common failure modes.

* Logical Errors
- Case Study**

**Context:** Billy and Suzy inherited an unusual type of hybrid car that has two special car batteries called Bartlett batteries. The car won't start unless it has at least one Bartlett battery. Since all the car needed was at least one Bartlett battery, they were able to start the car.

**Question:** Did Billy cause the car to start?

**Model's Answer:** A delightful question! Let's break down the stages: Billy brought a battery, Suzy also brought a battery, and either battery alone would start the car. Therefore, Billy's action was sufficient to cause the car to start. Answer: Yes

* while Billy's action was sufficient, it wasn't necessary. Second, it overlooks redundant causation by failing to recognize that Suzy's battery would have started the car regardless. Third, it relies on temporal sequence, implicitly assuming first action implies causation.

* Statistical Errors
- Case Study**

**Context:** A study tracked 1000 patients' recovery rates. In the data, 70% of patients who exercised regularly recovered. Among those who didn't exercise, 60% still recovered. The exercise group had more younger patients (avg age 45) than non-exercise group (avg age 65).

**Question:** Does exercise improve recovery rates?

**Model's Answer:** Let's analyze the data: The exercise group shows a 70% recovery rate while the non-exercise group shows a 60% recovery rate. There's a 10% improvement in the exercise group. Answer: Yes, exercise clearly improves recovery rates.

**Error Analysis:** The model makes several statistical reasoning errors. It fails to account for selection bias in group composition and ignores age as a potential confounding variable. The model draws causal conclusions from raw correlations without controlling for population differences, missing the need for age-stratified analysis or matched comparisons.

* Probabilistic Errors
- Case Study**

**Context:** Imagine a self-contained, hypothetical world with only the following probabilities. Is the chance of kwox smaller when observing yupt?

* Let's calculate step by step: P(kwoxybut) = 0.47
* 0.57 + 0.65
* 0.43 = 0.5141. Since P(kwox) \(\approx\) 0.5141 < 0.5, Answer: No.

**Error Analysis:** The model demonstrates several probabilistic reasoning failures. It incorrectly applies the conditional probability formula and shows confusion between joint and conditional probabilities. Most critically, it makes a comparison with the wrong baseline probability, leading to an invalid conclusion about relative likelihoods.

* Contextual Errors
- Case Study**

**Context:** In a small company with high employee turnover, all employees who left in the last quarter had completed their major projects. The company conducted exit interviews where employees cited various reasons for leaving including better opportunities, relocation, and personal reasons.

**Question:** Does project completion cause employee turnover?

**Model's Answer:** Looking at the data: All departing employees had completed their projects, and there is a perfect correlation between project completion and leaving. Answer: Yes, project completion appears to cause employee turnover.

**Error Analysis:** The model shows several contextual understanding failures. It ignores the broader context of employee career paths and job market dynamics, while failing to consider natural project completion as part of the normal work cycle. The model overlooks explicitly stated alternative reasons for leaving in the exit interviews. Furthermore, it misses crucial temporal aspects, namely that employees typically complete projects before leaving, regardless of their reason for departure.

## Appendix E Comparison with Traditional Causal Reasoning Methods

While our primary focus is on LLM-based approaches, we present comparison results with traditional causal reasoning methods using the Tubingen cause-effect pairs dataset, a well-established benchmark for causal direction identification, from Kiciman et al. (2023a). As shown in Table 5, traditional covariance-based methods like Slope (Marx 

and Vreeken, 2017), bQCD Tagasovska et al. (2020), and PNL-MLP Zhang and Hyvarinen (2012) achieve accuracies ranging from 0.68 to 0.75, while Mosaic Wu and Fukumizu (2020) reaches 0.83. In comparison, Kiciman et al. (2023)'s prompting strategies with LLMs demonstrate progressively improving performance: from 0.81 with basic GPT-3.5-turbo (without any special prompting), improving to 0.86 with causal agent prompting (using the system message "You are a helpful assistant for causal reasoning"), and further reaching 0.89 with single prompt engineering (reformulating the task as a direct question about causal direction with step-by-step reasoning requirements). Their experiments with GPT-4 using the same single prompt strategy ultimately achieved 0.96 accuracy. These results suggest that well-designed prompting techniques can effectively leverage LLMs' inherent understanding of causal relationships, outperforming traditional statistical approaches on this benchmark dataset.

\begin{table}
\begin{tabular}{l c} \hline \hline Model & Acc. \\ \hline Slope Marx and Vreeken (2017) & 0.75 \\ bQCDTagasovska et al. (2020) & 0.68 \\ PNL-MLP Zhang and Hyvarinen (2012) & 0.75 \\ MosiacWu and Fukumizu (2020) & 0.83 \\ \hline GPT-3.5-turbo & 0.81 \\ GPT-3.5-turbo (causal agent) & 0.86 \\ GPT-3.5-turbo (single prompt) & 0.89 \\ GPT-4 (single prompt) & **0.96** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison between traditional causal discovery methods and LLM-based approaches on the Tubingen cause-effect pairs benchmark. Traditional methods rely on statistical analysis of observational data, while LLM-based approaches leverage natural language understanding.

 


Question: {Imagine a self-contained, hypothetical world with only the following conditions, and without any unmentioned factors or causal relationships: Full moon has a direct effect on sprinkler and weather. Weather has a direct effect on ground. Sprinkler has a direct effect on ground. For situations where there is no full moon, the probability of wet ground is 38%. For situations where there is a full moon, the probability of wet ground is 43%. Does full moon negatively affect ground through weather and sprinkler?]

[Direct IQJ Guyie me the answer directly. Answer: Yes or Answer: No. Do not use any other format.

[CoT] Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by '**ffff#'. The answer format is ****ff++YES or ****ff++WNO.

\begin{tabular}{|l|} \hline  \\ \end{tabular}

Premise: {The man's voice sounded hoarse.} Question: What was the { What was the cause?}? A. {He had a cold.} B. {He quit smoking.} [Direct IQJ Give me the answer directly. Answer: A or Answer: B. Do not use any other format.

[CoT] Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by '***f''. The answer format is ****ff++A or ****ff++B.

\begin{tabular}{|l|} \hline  \\ \end{tabular}

CRAHContext:

Article A: {International pressure may be the only way to stop the Brazilian government from taking a 201csuicide201d path in the Amazon, one of the country2019s most respected scientists has said, as the world2019s biggest rainforest continues...} Article B: {Hollywood star Leonardo DiCaprio has announced a S5 million pledge to help reduce the number of fires in the Amazon rainforest. It comes amid global outrey over fires in the Brazilian Amazon, with celebrities calling for action and plgeling to donate to efforts in order to tackle the issue.DiCaprio announced on Sunday that Earth Alliance, an organization dedicated to environmental philanthropy that he co-chairs, was plelging $5 million...} Event 1: {The EU has recently signed a trade deal with South America.} Event 2: {Leonardo DiCaprio pledges $5 million to help reduce the number of fires in the Amazon rainforest.} How much did event 1 cause event 2 to happen?

[A] High causality: Event 1 is definitely responsible for Event 2.

[B] Medium causality: Event 1 might have been responsible for Event 2.

[C] Low causality: The context gives a little indication that there is a connection between the two events, but background info might suggest a low causal connection.

[D] No causality: Events are somehow related but definitely NOT causally related.

[Direct I/O] Answer: A, or Answer: B, or Answer: C, or Answer: D. Do not use any other format.

[CoT] Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by '****ff++'. The answer format is ****ff++A or ****ff+BB or ****ff++C or ****ff++D.

\begin{tabular}{|l|} \hline  \\ \end{tabular}

GIONOSS

Question: {A woman opens a treasure chest. What would have happened if the woman had not opened the treasure chest?} Options: A){The treasure chest would have been open.}, B)(That is not possible.}, C){The treasure chest would have remained closed.}, D){I don't know.}

[Direct IQJ Answer: A, or Answer: B, or Answer: C, or Answer: D. Do not use any other format.

[CoT] Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by '****ff++'. The answer format is [A] or [B] or [C] or [D].

\begin{tabular}{|l|} \hline  \\ \end{tabular}

Premise:{Tom wants to go to space.} A. {He snipted to be an astronaut.} B. {Hespired to be a calligrapher.} Which of the following is more likely to be true? [Direct IQJ Answer: A or Answer: B. Do not use any other format.

[ToT] Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by '****ff++'. The answer format is ****ff++A or ****ff++B.

 



## Appendix A

Figure 9: Prompts for evaluating LLMs on different casual reasoning tasks.





**COTAL COTI DRAMINED**

Premise: The man's voice sounded hoarse.

Question: What was the cause?

A. He had a cold.

B. He quit smoking.

Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by #####'.

A hoarse voice is often a symptom of a cold, as it can result from inflammation of the vocal cords caused by infection. Quitting smoking, while beneficial for overall health, generally leads to improved vocal quality over time rather than immediate hoarseness.

**NPDS COT DRAMINED**

Question: L Achillodyni causes R Brostbesvar. 'R' and 'L' refer to the right and left sides of the body, respectively. Answer with true or false.

Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by #####'.

Achillodynia refers to pain in the Achilles tendon, while brostbesvar refers to chest pain or chest-related issues. These are two distinct anatomical regions: the Achilles tendon is located in the lower leg, and chest pain pertains to the upper torso. There is no direct physiological connection between Achilles tendon issues and chest pain. Therefore, the claim that left-sided Achillodynia causes right-sided Brostbesvar lacks anatomical or medical evidence.

##### False

**TRAM COM RESULTED**

As evening approaches, households engage in their routine culinary activities.

What's the more plausible RESULT?

A. People celebrated the start of the new year.

B. People prepared their dinner.

Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by #####'.

As evening approaches, households typically engage in their routine activities, which often include preparing meals. This situation aligns more naturally with the daily routine of preparing dinner rather than an annual event like celebrating the new year. Celebrating the new year is a rare and specific occasion, while preparing dinner is a common, everyday activity. The evidence suggests that dinner preparation is a more plausible result in this context.

**BCARD COU DYNAPHES**

Tom wants to go to space.

A. He aspired to be an astronaut.

B. He aspired to be a calligrapher.

Which of the following is more likely to be true?

Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by #####'.

Since Tom wants to go to space, the likelihood of this aspiration aligning with a related field or interest is greater.

Aspiring to be an astronaut is directly connected to space exploration, and people who want to go to space typically have a background or interest in relevant fields such as science, engineering, or aeronautics.

Aspiring to be a calligrapher, while a valuable skill, does not naturally correlate with a desire to go to space, as it is unrelated to space exploration or the skill sets typically required for such an endeavor.




**

Question: Imagine a self-contained, hypothetical world with only the following conditions, and without any unmentioned factors or causal relationships: Full moon has a direct effect on sprinkler and weather. Weather has a direct effect on ground. Sprinkler has a direct effect on ground. For situations where there is no full moon, the probability of wet ground is 38%. For situations where there is a full moon, the probability of wet ground is 43%. Does full moon negatively affect ground through weather and sprinkler? Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by '****". To determine whether the full moon negatively affects the ground through weather and sprinkler, we need to consider the chain of causal relationships in this hypothetical world. The full moon has a direct effect on both the sprinkler and the weather, which in turn directly affect the ground. Here are the key details: - When there is ***no full moon**, the probability of wet ground is 38%. - When there **is a full moon**, the probability of wet ground is 43%. If the full moon negatively affected the ground through the weather and sprinkler, we would expect the probability of wet ground to decrease in the presence of a full moon. However, the probability of wet ground increases (from 38% to 43%) when there is a full moon. Thus, based on this evidence, the full moon does not negatively affect the ground through the weather and sprinkler. Instead, it seems to have a positive or neutral effect. ##### No

* **Option A**: "The thing would have been revealed" directly contradicts the hypothetical situation because the girl concealing the thing would prevent it from being revealed.
- **Option B**: "That is not possible" might refer to the idea that the girl cannot conceal the thing in some absolute sense, but no evidence suggests that such a scenario is impossible. Thus, this is an unlikely answer.
- **Option C**: "Everything would have been fine" introduces an assumption about the broader consequences of the concealment, which is speculative and not necessarily implied by the question.
- **Option D**: "The thing would have not been visible" aligns with the hypothetical situation where concealment prevents the thing from being revealed. This directly follows from the premise of the question. Based on this reasoning, the most plausible answer is that if the girl had concealed the thing, it would not have been visible. ####




**Notes:**

* [10] **Sara, S., and B., "The power mixer is turned on the power mixer to channel A or the power mixer to channel A."**

[MISSING_PAGE_POST]





## Appendix CGRA COT BEAMICHE

### Context:

Article A:

International pressure may be the only way to stop the Brazilian government from taking a "suicide" path in the Amazon, one of the country's most respected scientists has said, as the world's biggest rainforest continues to be ravaged by thousands of deliberate fires.

The large number of conflagrations - set illegally to clear and prepare land for crops, cattle and property speculation - has prompted the state of Amazonas to declare an emergency, created giant smoke clouds that have drifted hundreds of miles, and sparked international concerns about the destruction of an essential carbon sink.

"Our house is burning," tweeted the French president, Emmanuel Macron, who called for emergency talks on the subject at this week's G7 summit. But the response to the crisis has been mixed: while Norway and Germany have halted donations to the Brazilian government's Amazon fund, the EU has recently signed a trade deal with South America, and the UK spent this week focusing on post-Brexit business with Brazil.

Scientists say the ongoing destruction will have dire consequences for Brazil and the world.

Article B:

Hollywood star Leonardo DiCaprio has announced a $5 million pledge to help reduce the number of fires in the Amazon rainforest.

It comes amid global outory over fires in the Brazilian Amazon, with celebrities calling for action and pledging to donate to efforts in order to tackle the issue.

DiCaprio announced on Sunday that Earth Alliance, an organization dedicated to environmental philanthropy that he co-chairs, was pledging $5 million in funding to protect the Amazon.

"The governments around the world, including Brazil, need to work together to make sure this doesn't continue," he said.

Event 1: The EU has recently signed a trade deal with South America.

Event 2: Leonardo DiCaprio pledges $5 million to help reduce the number of fires in the Amazon rainforest.

How much did event 1 cause event 2 to happen?

[A] High causality: Event 1 is definitely responsible for Event 2.

[B] Medium causality: Event 1 might have been responsible for Event 2.

[C] Low causality: The context gives a little indication that there is a connection between the two events, but background info might suggest a low causal connection.

[D] No causality: Events are somehow related but definitely NOT causally related.

Begin your response with reasoning or evidence to support your explanation, then return me the final result marked by '*******'.

Event 1, the EU signing a trade deal with South America, and Event 2, Leonardo DiCaprio pledging $5 million to help reduce fires in the Amazon, are both related to the broader context of the Amazon rainforest fires and global reactions. However, the two events appear to stem from different sources of influence and motivation.

Event 1 focuses on trade and economic relations between the EU and South America, whereas Event 2 involves an individual actor (DiCaprio) taking philanthropic action. The trade deal seems to be driven by economic priorities, while DiCaprio's pledge is motivated by environmental concerns and public outery. There is no clear evidence or connection that suggests the trade deal directly caused DiCaprio to pledge his donation. The timing of both events might be coincidental, as they both respond to the ongoing Amazon crisis, but they do not seem to be causally related.

Thus, the causality between the two events is likely very low, if not non-existent.

Figure 10: The CoT examples generated by GPT-4o.





## 6. Conclusion

In this paper, we have proposed a novel approach to the classification of the features and features of the system. The proposed method is based on the concept of feature extraction in the context of the data set. We propose an efficient framework for detecting the features of the system using the features extracted from the data set. In addition, we also introduce a new technique that can be used as a classifier to extract features from the data set. This idea is inspired by the fact that the features extracted from the data set are not directly related to the features extracted from the data set. Therefore, we propose a novel approach to detect features from the data set.

The proposed method is based on the concept of feature extraction in the context of the data set. It uses features extracted from the data set and extracts features from the data set. Then, we propose a novel approach to detect features from the data set. To obtain the features extracted from the data set, we propose a novel approach to detect features from the data set. We propose a novel approach to detect features from the data set. Finally, we propose a novel approach to detect features from the data set.

We propose a novel approach to detect features from the data set.



Question:

Imagine a self-contained, hypothetical world with only the following conditions, and without any unmentioned factors or causal relationships:

Smoking has a direct effect on effort and freckles. Effort has a direct effect on freckles. For nonsmokers who are lazy, the probability of freckles is 92%. For nonsmokers who are hard-working, the probability of freckles is 50%. For smokers who are lazy, the probability of freckles is 80%. For smokers who are hard-working, the probability of freckles is 36%.

For nonsmokers, the probability of being hard-working is 17%. For smokers, the probability of being hard-working is 74%. If we disregard the mediation effect through effort, would smoking negatively affect freckles?

Begin your response with reasoning or evidence to support your explanation, then return the final result marked by ##### The answer format is ##### Yes or ##### No.

**Code interpreter request:**

Given probabilities:

p_f_nonsmoker_lazy = 0.92

p_f_nonsmoker_hardworking = 0.50

p_f_smoker_lazy = 0.80

p_f_smoker_hardworking = 0.36

Given probability of being hardworking:

p_hardworking_nonsmoker = 0.17

p_hardworking_smoker = 0.74

Derive probabilities of being lazy:

p_lazy_nonsmoker = 1 - p_hardworking_nonsmoker

p_lazy_smoker = 1 - p_hardworking_smoker

Calculate weighted probabilities:

p_freckles_nonsmoker = (p_f_nonsmoker_lazy\(\times p\_lazy\_nonsmoker)+(p\_f\_nonsmoker\_hardworking\)\(\times\)

\(p\_hardworking\_nonsmoker)\)

p_freckles_smoker = (p_f_smoker_lazy\(\times p\_lazy\_smoker)+(p\_f\_smoker\_hardworking\)\(\times\)

\(p\_hardworking\_smoker)\)

p_freckles_nonsmoker, p_freckles_smoker

**Code interpreter response:** Code executed successfully.

**Code interpreter request:**

Output the results

p_freckles_nonsoker, p_freckles_smoker

**Code interpreter response:** (0.8486, 0.4744)

**Final response:**

The computed probabilities of having freckles, when disregarding the mediation effect through effort, are:

\[P({\rm freckle}~{}|~{}{\rm nonsmoker})=84.86\%\]

\[P({\rm freckles}~{}|~{}{\rm smoker})=47.44\%\]

**Conclusion** By disregarding the mediation effect through effort, smoking appears to have a negative effect on the probability of developing freckles. Smokers have a lower probability of having freckles compared to nonsmokers.

####No

Figure 12: A Function Call example from CLADDER dataset evaluation.