Causal Reasoning in Pieces: Modular In-Context Learning for Causal Discovery

TL;DR

This paper demonstrates that modular in-context reasoning pipelines significantly enhance causal discovery performance in large language models, leveraging emergent reasoning capabilities and structured prompts to improve robustness and generalization.

Contribution

Introduces a modular in-context reasoning pipeline inspired by Tree-of-Thoughts and Chain-of-Thoughts to improve causal discovery in large language models, achieving substantial performance gains.

Findings

Reasoning-first architectures outperform prior approaches on Corr2Cause benchmark.

Structured in-context pipelines nearly triple baseline performance.

Analysis shows reasoning chain length and complexity impact effectiveness.

Abstract

Causal inference remains a fundamental challenge for large language models. Recent advances in internal reasoning with large language models have sparked interest in whether state-of-the-art reasoning models can robustly perform causal discovery-a task where conventional models often suffer from severe overfitting and near-random performance under data perturbations. We study causal discovery on the Corr2Cause benchmark using the emergent OpenAI's o-series and DeepSeek-R model families and find that these reasoning-first architectures achieve significantly greater native gains than prior approaches. To capitalize on these strengths, we introduce a modular in-context pipeline inspired by the Tree-of-Thoughts and Chain-of-Thoughts methodologies, yielding nearly three-fold improvements over conventional baselines. We further probe the pipeline's impact by analyzing reasoning chain length, complexity, and conducting qualitative and quantitative comparisons between conventional and reasoning models. Our findings suggest that while advanced reasoning models represent a substantial leap forward, carefully structured in-context frameworks are essential to maximize their capabilities and offer a generalizable blueprint for causal discovery across diverse domains.