Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies

TL;DR

This paper develops a method to estimate the causal impact of multiple review aspects on business outcomes using multi-modal proxies, addressing confounding in observational online review data.

Contribution

It introduces a novel approach leveraging multi-modal proxies to identify and estimate causal effects of review aspects in observational data.

Findings

Effective estimation of causal effects demonstrated on synthetic data.

Method provides actionable insights into how aspect improvements influence business metrics.

Empirical validation confirms robustness and practical utility.

Abstract

Online reviews enable consumers to engage with companies and provide important feedback. Due to the complexity of the high-dimensional text, these reviews are often simplified as a single numerical score, e.g., ratings or sentiment scores. This work empirically examines the causal effects of user-generated online reviews on a granular level: we consider multiple aspects, e.g., the Food and Service of a restaurant. Understanding consumers' opinions toward different aspects can help evaluate business performance in detail and strategize business operations effectively. Specifically, we aim to answer interventional questions such as What will the restaurant popularity be if the quality w.r.t. its aspect Service is increased by 10%? The defining challenge of causal inference with observational data is the presence of "confounder", which might not be observed or measured, e.g., consumers' preference to food type, rendering the estimated effects biased and high-variance. To address this challenge, we have recourse to the multi-modal proxies such as the consumer profile information and interactions between consumers and businesses. We show how to effectively leverage the rich information to identify and estimate causal effects of multiple aspects embedded in online reviews. Empirical evaluations on synthetic and real-world data corroborate the efficacy and shed light on the actionable insight of the proposed approach.