Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings
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Abstract (arXiv)
Online reviews provide valuable insights into the perceived quality of facets of a product or service. While aspect-based sentiment analysis has focused on extracting these facets from reviews, there is less work understanding the impact of each aspect on overall perception. This is particularly challenging given correlations among aspects, making it difficult to isolate the effects of each. This paper introduces a methodology based on recent advances in text-based causal analysis, specifically CausalBERT, to disentangle the effect of each factor on overall review ratings. We enhance CausalBERT with three key improvements: temperature scaling for better calibrated treatment assignment estimates; hyperparameter optimization to reduce confound overadjustment; and interpretability methods to characterize discovered confounds. In this work, we treat the textual mentions in reviews as proxies for real-world attributes. We validate our approach on real and semi-synthetic data from over 600K reviews of U.S. K-12 schools. We find that the proposed enhancements result in more reliable estimates, and that perception of school administration and performance on benchmarks are significant drivers of overall school ratings.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 30 references that are in the map. First posted on arXiv 2 Jun 2026.
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13 of 30 references.
References · 13
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2017On Calibration of Modern Neural Networkscited by 4,960
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
- 2020CausaLM: Causal Model Explanation Through Counterfactual Language Modelscited by 117
- 2020Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimatescited by 59
- 2020Causal Effects of Linguistic Propertiescited by 39
- 2016A Hierarchical Model of Reviews for Aspect-based Sentiment Analysiscited by 21
- 2018Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Qualitycited by 21
- 2019Estimating Causal Effects of Tone in Online Debatescited by 19
- 2020Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inferencecited by 13
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- 2023RCT Rejection Sampling for Causal Estimation Evaluationcited by 7
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Citing papers per year: 2026: 1.
1 paper.