Estimating Causal Effects of Text Interventions Leveraging LLMs

ArXi:2410.21474v3 Announce Type: replace-cross 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.