Exploring the Potential of Large Language Models in Generating Saturated DAGs for Causal Inference

This talk investigates whether large language models (LLMs) could potentially assist in the creation of "saturated DAGs", graphical representations that exhaustively map all possible causal pathways in a system. We'll critically examine if and how LLMs might help identify the full space of plausible causal relationships that traditional approaches may overlook. The presentation will assess the strengths and limitations of prompting LLMs to generate comprehensive causal structures, identify backdoor paths, and navigate complex causal systems.

October 14, 2025

12:30 PM – 1:00 PM

IDWSDS 2025