Causal inference from texts: a random-forest approach

Abstract

This paper employs causal random forests to analyse textual reviews in an e-commerce context, specifically investigating the causal impact of sentiment on the Positive Feedback Count (PFC). The PFC denotes the number of users who found the review helpful. The results uncover a negative causal effect, indicating that tran- sitioning from negative to positive sentiment reduces the count of users perceiving a review as helpful. The analysis further explores heterogeneity, highlighting the nu- anced influence of specific words and variations in treatment effects. This research underscores the efficacy of causal inference in elucidating the intricate dynamics between sentiment and the perceived utility of reviews.