CausalDS: Benchmarking Causal Reasoning in Data-Science Agents

2026-07-09

AI Summary

Existing benchmarks struggle to comprehensively evaluate the causal reasoning capabilities of data science agents, often lacking either realistic analytical scenarios or rigorous causal generative mechanisms. To address this gap, this work proposes CausalDS, a novel benchmark that systematically integrates structural causal models (SCMs), realistic synthetic data, and natural language narratives. CausalDS spans Pearl’s three levels of causal reasoning and is embedded within an authentic data science workflow, supporting tool invocation, programming interfaces, and uncertainty quantification. By grounding scenario generation in real-world data distributions, the benchmark mitigates the “causal parroting” problem and explicitly incorporates active abstention as a core evaluation dimension, thereby enabling a holistic assessment of agents’ abilities in causal inference, practical execution, and principled refusal to answer.

Abstract

Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks without realistic data analysis or data analysis benchmarks without a principled causal data-generating structure. Furthermore, existing causal evaluation datasets are often restricted to curated examples from existing sources, with diversity coming from limited templatized variations rather than from systematic generation of novel synthetic causal structures. We introduce CausalDS, a benchmark for evaluating causal reasoning in agentic data-science workflows. Each benchmark instance is a scene consisting of a sampled structural causal model (SCM) with generated observational data and an accompanying synthetic natural-language story grounded in a realistic domain. We optionally ground the composition of the benchmark components in empirical distributions obtained from real-world datasets, thus retaining empirical structure while reducing the "causal parrot" risk through completely synthetic generation. From each scene, we then derive tasks spanning all three of Pearl's rungs, with typical data-science prediction tasks appearing as Rung 1. Most tasks include a data science coding component, where the model typically needs to use several tools to arrive at the final answer due to the frequent presence of imperfect observations, which are generated by an observation model. Additionally, recognizing when a question admits no warranted answer and abstaining is treated as a first-class scored outcome. The benchmark thus jointly evaluates symbolic causal reasoning, data science, uncertainty quantification, abstention, and tool use/coding.

Problem

• causal reasoning
• data-science agents
• benchmarking
• structural causal model
• synthetic data

Innovation

• Causal Reasoning
• Structural Causal Model
• Data-Science Agents
• Synthetic Benchmark
• Tool Use