  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.
