Workshop on Causality and (Large) Language Models - Session 2, Estimating Causal Effects with Text Data

Presented by DFI postdoctoral researcher, Amir Feder.

The intersection of causal inference and language models has grown in importance as advances in large language models (LLMs) open new opportunities for research and application. This workshop provides a structured exploration of how these fields converge, focusing on foundational techniques, challenges, and state-of-the-art methodologies for leveraging causal inference with text data and improving LLMs through causal reasoning.

Open only to Columbia Business School Doctoral (PhD) students and faculty.

Registration for March 28.