# Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings

arXiv: https://arxiv.org/abs/2606.04286 · HTML: https://mlsanity.com/paper/2606.04286/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [CausaLM: Causal Model Explanation Through Counterfactual Language Models](https://mlsanity.com/paper/2005.13407/) (2020)
- [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://mlsanity.com/paper/2005.00649/) (2020)
- [Causal Effects of Linguistic Properties](https://mlsanity.com/paper/2010.12919/) (2020)
- [A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1609.02745/) (2016)
- [Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality](https://mlsanity.com/paper/1801.00644/) (2018)
- [Estimating Causal Effects of Tone in Online Debates](https://mlsanity.com/paper/1906.04177/) (2019)
- [Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference](https://mlsanity.com/paper/2009.09961/) (2020)
- [RCT Rejection Sampling for Causal Estimation Evaluation](https://mlsanity.com/paper/2307.15176/) (2023)

## Built on by (most cited first, 1 of 1)

- [The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text](https://mlsanity.com/paper/2607.26309/) (2026)

## 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.