mlsanityData to 13 Oct 2026

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

arXiv 2112.10274 ↗First posted 19 Dec 2021cs.LG cs.AI stat.MEcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 20 of its 52 references that are in the map. First posted on arXiv 19 Dec 2021.

Builds on

20 of 52 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2024Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Mediacited by 7
  2. 2023Causal Inference from Text: Unveiling Interactions between Variablescited by 5
  3. 2024CausalMMM: Learning Causal Structure for Marketing Mix Modelingcited by 5
  4. 2024Estimating Causal Effects of Text Interventions Leveraging LLMscited by 4