Primary Submission Category: Machine Learning and Causal Inference
Causal Inference with Multiple Latent Textual Treatments
Authors: Arisa Sadeghpour,
Presenting Author: Arisa Sadeghpour*
Researchers are increasingly interested in understanding the causal effect of texts on human behavior, e.g. the effect of social media posts on persuasion. Recent work introduces a framework for estimating the isolated causal effect of focal language, adjusting for other, non-focal attributes of the text (Lin et al., 2025). While this framework considers settings with only one focal attribute, there are often several focal treatments of interest within texts. As with factorial studies, interaction effects of these focal treatments are often of interest. We leverage recent advances in observational factorial studies (Yu & Ding, 2026) to identify and estimate the isolated causal effects of multiple focal treatments and their interactions. We demonstrate the proposed approach through simulation and applications and offer practical guidance for estimation.