CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
#CausalReasoningBenchmark #causal inference #identification #estimation #benchmark #machine learning #AI evaluation #research design
📌 Key Takeaways
- New benchmark disentangles identification and estimation in causal analysis
- Contains 173 queries across 138 real-world datasets from academic sources
- Enables granular diagnosis of failures in reasoning vs numerical execution
- LLM testing shows bottleneck is in research design details, not computation
📖 Full Retelling
Researchers Ayush Sawarni, Jiyuan Tan, and Vasilis Syrgkanis introduced the CausalReasoningBenchmark on February 24, 2026, a comprehensive evaluation tool designed to address critical limitations in assessing automated causal inference systems. The benchmark consists of 173 queries across 138 real-world datasets, curated from 85 peer-reviewed research papers and four widely-used causal-inference textbooks. Unlike existing benchmarks that rely on single numerical outputs like Average Treatment Effects (ATE), this new approach disentangles the two fundamental steps in causal analysis: identification (formulating valid research designs under stated assumptions) and estimation (implementing those designs numerically on finite data). For each query, systems must produce both a structured identification specification detailing strategies, variables, and design elements, plus a point estimate with standard error. By scoring these components separately, the benchmark enables precise diagnosis of whether failures occur in causal reasoning or numerical execution. Initial testing with a state-of-the-art large language model revealed that while the model correctly identified high-level strategies in 84% of cases, full identification-specification correctness dropped to only 30%, indicating that research design nuances pose greater challenges than computation. The CausalReasoningBenchmark is now publicly available on Hugging Face, with the goal of fostering development of more robust automated causal-inference systems.
🏷️ Themes
Artificial Intelligence, Causal Inference, Benchmarking, Research Evaluation
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.20571 [Submitted on 24 Feb 2026] Title: CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation Authors: Ayush Sawarni , Jiyuan Tan , Vasilis Syrgkanis View a PDF of the paper titled CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation, by Ayush Sawarni and 2 other authors View PDF HTML Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect . 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 a structured identification specification that names the strategy, the treatment, outcome, and control variables, and all design-specific elements, and 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20571 [cs.AI] (or arXiv:2602.20571v1 [cs.AI] ...
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