Ice Cream Doesn't Cause Drowning: Benchmarking LLMs Against Statistical Pitfalls in Causal Inference

2025-05-19

AI Summary

Large language models (LLMs) exhibit significant deficiencies in rigorous statistical causal reasoning—particularly regarding paradoxes (e.g., Simpson’s paradox) and biases (e.g., selection bias)—posing risks in high-stakes domains like healthcare and policy. Method: We introduce CausalPitfalls, the first benchmark explicitly designed to evaluate statistical causal reliability. It features a multi-level structured challenge set of causal pitfalls, a novel executable Python/Statsmodels code-assisted reasoning protocol, and an automated scoring mechanism tightly aligned with human expert judgments. Our evaluation integrates direct prompting, code generation, and principled, rule-based statistical scoring. Results: Experiments reveal that state-of-the-art LLMs perform substantially below acceptable thresholds in identifying and avoiding causal pitfalls. This work not only exposes a critical limitation of current LLMs in safety-critical applications but also establishes the first reproducible, scalable quantitative evaluation framework—with baseline results—to advance causal-aware LLM assessment and alignment research.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Reliable causal inference is essential for making decisions in high-stakes areas like medicine, economics, and public policy. However, it remains unclear whether large language models (LLMs) can handle rigorous and trustworthy statistical causal inference. Current benchmarks usually involve simplified tasks. For example, these tasks might only ask LLMs to identify semantic causal relationships or draw conclusions directly from raw data. As a result, models may overlook important statistical pitfalls, such as Simpson's paradox or selection bias. This oversight limits the applicability of LLMs in the real world. To address these limitations, we propose CausalPitfalls, a comprehensive benchmark designed to rigorously evaluate the capability of LLMs in overcoming common causal inference pitfalls. Our benchmark features structured challenges across multiple difficulty levels, each paired with grading rubrics. This approach allows us to quantitatively measure both causal reasoning capabilities and the reliability of LLMs' responses. We evaluate models using two protocols: (1) direct prompting, which assesses intrinsic causal reasoning, and (2) code-assisted prompting, where models generate executable code for explicit statistical analysis. Additionally, we validate the effectiveness of this judge by comparing its scoring with assessments from human experts. Our results reveal significant limitations in current LLMs when performing statistical causal inference. The CausalPitfalls benchmark provides essential guidance and quantitative metrics to advance the development of trustworthy causal reasoning systems.

Problem

• Evaluating LLMs' ability to handle statistical causal inference pitfalls
• Assessing reliability of LLMs in overcoming biases like Simpson's paradox
• Benchmarking LLMs' causal reasoning across structured difficulty levels

Innovation

• CausalPitfalls benchmark evaluates LLMs rigorously
• Structured challenges with grading rubrics introduced
• Direct and code-assisted prompting protocols used