Estimating Causal Effects of Text Interventions Leveraging LLMs
This work addresses the problem of causal inference for high-dimensional text data in social systems, offering a flexible approach for researchers and practitioners, though it appears incremental as it builds on existing domain adaptation and LLM techniques.
The paper tackles the challenge of estimating causal effects of textual interventions in social systems, such as reducing anger in posts to impact engagement, by proposing CausalDANN, a novel method that leverages LLMs for text transformations and achieves robust effect estimates against domain shifts.
Quantifying the effects of textual interventions in social systems, such as reducing anger in social media posts to see its impact on engagement, is challenging. Real-world interventions are often infeasible, necessitating reliance on observational data. Traditional causal inference methods, typically designed for binary or discrete treatments, are inadequate for handling the complex, high-dimensional textual data. This paper addresses these challenges by proposing CausalDANN, a novel approach to estimate causal effects using text transformations facilitated by large language models (LLMs). Unlike existing methods, our approach accommodates arbitrary textual interventions and leverages text-level classifiers with domain adaptation ability to produce robust effect estimates against domain shifts, even when only the control group is observed. This flexibility in handling various text interventions is a key advancement in causal estimation for textual data, offering opportunities to better understand human behaviors and develop effective interventions within social systems.