# Causal Estimation for Text Data with (Apparent) Overlap Violations

arXiv: https://arxiv.org/abs/2210.00079 · HTML: https://mlsanity.com/paper/2210.00079/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [How to Make Causal Inferences Using Texts](https://mlsanity.com/paper/1802.02163/) (2018)
- [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://mlsanity.com/paper/2005.00649/) (2020)
- [Double/Debiased Machine Learning for Treatment and Causal Parameters](https://mlsanity.com/paper/1608.00060/) (2016)
- [Causal Effects of Linguistic Properties](https://mlsanity.com/paper/2010.12919/) (2020)
- [Challenges of Using Text Classifiers for Causal Inference](https://mlsanity.com/paper/1810.00956/) (2018)
- [The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter](https://mlsanity.com/paper/1405.1438/) (2014)
- [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)
- [Quantifying the Causal Effects of Conversational Tendencies](https://mlsanity.com/paper/2009.03897/) (2020)
- [Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap](https://mlsanity.com/paper/2104.05762/) (2021)
- [Adapting Text Embeddings for Causal Inference](https://mlsanity.com/paper/1905.12741/) (2019)
- [Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges](https://mlsanity.com/paper/1702.01250/) (2017)
- [When do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception using Individual Treatment Effect Estimation](https://mlsanity.com/paper/1811.04890/) (2018)

## Built on by (most cited first, 15 of 15)

- [Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments](https://mlsanity.com/paper/2410.00903/) (2024)
- [A Reality Check on Context Utilisation for Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.17031/) (2024)
- [RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals](https://mlsanity.com/paper/2410.11348/) (2024)
- [Positivity-free Policy Learning with Observational Data](https://mlsanity.com/paper/2310.06969/) (2023)
- [Deep Autoregressive Models as Causal Inference Engines](https://mlsanity.com/paper/2409.18581/) (2024)
- [Preference Learning for AI Alignment: a Causal Perspective](https://mlsanity.com/paper/2506.05967/) (2025)
- [Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers](https://mlsanity.com/paper/2305.16863/) (2023)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [Can Large Language Models (or Humans) Disentangle Text?](https://mlsanity.com/paper/2403.16584/) (2024)
- [A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?](https://mlsanity.com/paper/2510.08758/) (2025)
- [Leveraging Generative Artificial Intelligence for Causal Inference with Unstructured Data](https://mlsanity.com/paper/2507.03897/) (2025)
- [What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?](https://mlsanity.com/paper/2403.16142/) (2024)
- [DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects](https://mlsanity.com/paper/2505.00961/) (2025)
- [Causal Effect Estimation with Latent Textual Treatments](https://mlsanity.com/paper/2602.15730/) (2026)
- [The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text](https://mlsanity.com/paper/2607.26309/) (2026)

## Abstract (arXiv)

Consider the problem of estimating the causal effect of some attribute of a text document; for example: what effect does writing a polite vs. rude email have on response time? To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and outcome -- e.g., the topic or writing level of the text. These confounding aspects are unknown a priori, so it seems natural to adjust for the entirety of the text (e.g., using a transformer). However, causal identification and estimation procedures rely on the assumption of overlap: for all levels of the adjustment variables, there is randomness leftover so that every unit could have (not) received treatment. Since the treatment here is itself an attribute of the text, it is perfectly determined, and overlap is apparently violated. The purpose of this paper is to show how to handle causal identification and obtain robust causal estimation in the presence of apparent overlap violations. In brief, the idea is to use supervised representation learning to produce a data representation that preserves confounding information while eliminating information that is only predictive of the treatment. This representation then suffices for adjustment and can satisfy overlap. Adapting results on non-parametric estimation, we find that this procedure is robust to conditional outcome misestimation, yielding a low-bias estimator with valid uncertainty quantification under weak conditions. Empirical results show strong improvements in bias and uncertainty quantification relative to the natural baseline.