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CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation

arXiv:2602.20571v1 Announce Type: new Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect (ATE). This approach conflates two distinct steps in causal analysis: identification-formulating a valid research design under stated assumptions-and estimation-implementing that design numerically on finite data. We introduce CausalReasoningBenchmark, a benchmark of 173 queries across 138 real-world datasets, curated from 85 peer-reviewed research papers and four widely-used causal-inference textbooks. For each query a system must produce (i) a structured identification specification that names the strategy, the treatment, outcome, and control variables, and all design-specific elements, and (ii) a point estimate with a standard error. By scoring these two components separately, our benchmark enables granular diagnosis: it distinguishes failures in causal reasoning from errors in numerica

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Ayush Sawarni, Jiyuan Tan, Vasilis Syrgkanis · · 1 min read · 20 views
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arXiv:2602.20571v1 Announce Type: new Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect (ATE). This approach conflates two distinct steps in causal analysis: identification-formulating a valid research design under stated assumptions-and estimation-implementing that design numerically on finite data. We introduce CausalReasoningBenchmark, a benchmark of 173 queries across 138 real-world datasets, curated from 85 peer-reviewed research papers and four widely-used causal-inference textbooks. For each query a system must produce (i) a structured identification specification that names the strategy, the treatment, outcome, and control variables, and all design-specific elements, and (ii) a point estimate with a standard error. By scoring these two components separately, our benchmark enables granular diagnosis: it distinguishes failures in causal reasoning from errors in numerical execution. Baseline results with a state-of-the-art LLM show that, while the model correctly identifies the high-level strategy in 84 % of cases, full identification-specification correctness drops to only 30 %, revealing that the bottleneck lies in the nuanced details of research design rather than in computation. CausalReasoningBenchmark is publicly available on Hugging Face and is designed to foster the development of more robust automated causal-inference systems.

Executive Summary

This article introduces CausalReasoningBenchmark, a comprehensive real-world benchmark for evaluating automated causal inference systems. The benchmark comprises 173 queries across 138 real-world datasets, curated from 85 peer-reviewed research papers and four widely-used causal-inference textbooks. The benchmark assesses a system's ability to produce both a structured identification specification and a point estimate with a standard error. Baseline results with a state-of-the-art LLM demonstrate that the model excels in high-level strategy identification but struggles with nuanced details of research design. The benchmark is publicly available and designed to foster the development of more robust automated causal-inference systems.

Key Points

  • ▸ CausalReasoningBenchmark is a real-world benchmark for evaluating automated causal inference systems
  • ▸ The benchmark assesses a system's ability to produce both a structured identification specification and a point estimate with a standard error
  • ▸ Baseline results highlight the struggle of state-of-the-art LLMs with nuanced details of research design

Merits

Strength

The benchmark's comprehensive coverage of real-world datasets and peer-reviewed research papers provides a robust evaluation framework for automated causal inference systems.

Comprehensive Evaluation

The benchmark's dual-component evaluation (identification specification and point estimate) enables granular diagnosis and distinguishes failures in causal reasoning from errors in numerical execution.

Demerits

Limitation

The benchmark's reliance on existing research papers and textbooks may introduce bias towards specific research domains or methodologies.

Scalability

The benchmark's large number of queries and datasets may pose scalability challenges for certain evaluation systems.

Expert Commentary

CausalReasoningBenchmark represents a significant step forward in the evaluation of automated causal inference systems. By providing a comprehensive and granular evaluation framework, the benchmark highlights the limitations of current state-of-the-art LLMs and offers a roadmap for future research. However, the benchmark's reliance on existing research papers and textbooks raises concerns about potential bias and scalability challenges. Nevertheless, the benchmark's potential to inform the development of more robust automated causal-inference systems and contribute to policy discussions surrounding their use make it a valuable contribution to the field.

Recommendations

  • ✓ Researchers and practitioners should utilize CausalReasoningBenchmark to evaluate and improve their automated causal-inference systems.
  • ✓ Future research should focus on addressing the limitations of the benchmark, including bias and scalability challenges, to create an even more comprehensive evaluation framework.

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