Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot
This addresses the problem of complex causal inference for practitioners in fields like healthcare and economics, though it is incremental as it builds on existing methods with interactive assistance.
The paper tackles the limited adoption of causal inference methods by introducing CATE-B, an open-source co-pilot system that uses LLMs to guide users through treatment effect estimation, resulting in a tool that lowers the barrier to rigorous causal analysis.
Estimating treatment effects (TE) from observational data is a critical yet complex task in many fields, from healthcare and economics to public policy. While recent advances in machine learning and causal inference have produced powerful estimation techniques, their adoption remains limited due to the need for deep expertise in causal assumptions, adjustment strategies, and model selection. In this paper, we introduce CATE-B, an open-source co-pilot system that uses large language models (LLMs) within an agentic framework to guide users through the end-to-end process of treatment effect estimation. CATE-B assists in (i) constructing a structural causal model via causal discovery and LLM-based edge orientation, (ii) identifying robust adjustment sets through a novel Minimal Uncertainty Adjustment Set criterion, and (iii) selecting appropriate regression methods tailored to the causal structure and dataset characteristics. To encourage reproducibility and evaluation, we release a suite of benchmark tasks spanning diverse domains and causal complexities. By combining causal inference with intelligent, interactive assistance, CATE-B lowers the barrier to rigorous causal analysis and lays the foundation for a new class of benchmarks in automated treatment effect estimation.