Estimating Causal Effects of Text Interventions Leveraging LLMs

2024-10-28

arXiv.org

AI Summary

Quantifying the causal effects of textual interventions (e.g., reducing anger expression on social media) in socio-technical systems remains challenging due to ethical and logistical constraints on real-world interventions, the high dimensionality and semantic complexity of text, and the reliance of conventional methods on binary or discrete treatment assumptions. To address this, we propose CausalDANN—a novel framework enabling causal effect estimation for *arbitrary* textual interventions. CausalDANN leverages large language models to generate diverse counterfactual texts and integrates domain-adversarial neural networks (DANN) with a text-level classifier, enabling robust causal inference using only observational control-group data. Its key innovation lies in relaxing the restrictive discrete-treatment assumption and achieving strong robustness to domain shift. Extensive experiments across multiple textual intervention scenarios demonstrate significant improvements in causal effect estimation accuracy. CausalDANN establishes a new, interpretable, and scalable paradigm for causal analysis of social-behavioral interventions.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Estimating causal effects of text interventions using LLMs
• Overcoming challenges of high-dimensional textual data in causal inference
• Enabling flexible text interventions for robust effect estimation

Innovation

• Uses LLMs for text transformation in causal estimation
• Leverages domain-adaptive text-level classifiers
• Handles arbitrary textual interventions flexibly