Can LLMs Serve as Causal Inference Agents? A Study on Post-Training Methods

Junqi Chen, Sirui Chen, Chaochao Lu

Keywords: Large Language Models (LLMs), Causal Inference, Post-training

Abstract:

Causal inference is essential for decision-making but remains challenging for non-experts. While large language models (LLMs) show promise in this domain, their precise causal estimation capabilities are still limited, and the impact of post-training on these abilities is insufficiently explored. This paper examines the extent to which post-training can enhance LLMs’ capacity for causal inference.
We introduce CausaGym, a comprehensive dataset comprising seven core causal tasks for training and five diverse test sets. Using this dataset, five post-training approaches—SFT, DPO, KTO, PPO, and GRPO are systematically evaluated.
Across five in-domain and four existing benchmarks, our experiments demonstrate that appropriate post-training enables smaller LLMs to perform causal inference competitively, often surpassing much larger models. Our 14B-parameter model achieves 93.5% accuracy on the CaLM benchmark, compared to 55.4% by OpenAI o3. Furthermore, the post-trained agents exhibit strong generalization and robustness under real-world conditions such as distribution shifts and noisy data. Collectively, these findings provide the first systematic evidence that targeted post-training can produce reliable and robust LLM-based causal inference agents.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 24888