# Challenges of Using Text Classifiers for Causal Inference

arXiv: https://arxiv.org/abs/1810.00956 · HTML: https://mlsanity.com/paper/1810.00956/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2018.

## Built on by (most cited first, 30 of 33)

- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021)
- [I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews](https://mlsanity.com/paper/2104.06893/) (2021)
- [A Causal Lens for Controllable Text Generation](https://mlsanity.com/paper/2201.09119/) (2022)
- [Neuro-Symbolic Procedural Planning with Commonsense Prompting](https://mlsanity.com/paper/2206.02928/) (2022)
- [On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs](https://mlsanity.com/paper/2407.19200/) (2024)
- [Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction](https://mlsanity.com/paper/2402.02171/) (2024)
- [Causal Intervention Improves Implicit Sentiment Analysis](https://mlsanity.com/paper/2208.09329/) (2022)
- [Causal Estimation for Text Data with (Apparent) Overlap Violations](https://mlsanity.com/paper/2210.00079/) (2022)
- [Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis](https://mlsanity.com/paper/2104.09420/) (2021)
- [Enhancing Model Robustness and Fairness with Causality: A Regularization Approach](https://mlsanity.com/paper/2110.00911/) (2021)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Generating Synthetic Text Data to Evaluate Causal Inference Methods](https://mlsanity.com/paper/2102.05638/) (2021)
- [Unsupervised Editing for Counterfactual Stories](https://mlsanity.com/paper/2112.05417/) (2021)
- [RCT Rejection Sampling for Causal Estimation Evaluation](https://mlsanity.com/paper/2307.15176/) (2023)
- [Proximal Causal Inference With Text Data](https://mlsanity.com/paper/2401.06687/) (2024)
- [CausalNLP: A Practical Toolkit for Causal Inference with Text](https://mlsanity.com/paper/2106.08043/) (2021)
- [Causal Inference from Text: Unveiling Interactions between Variables](https://mlsanity.com/paper/2311.05286/) (2023)
- [Bayesian Topic Regression for Causal Inference](https://mlsanity.com/paper/2109.05317/) (2021)
- [Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspects](https://mlsanity.com/paper/2109.07542/) (2021)
- [CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors](https://mlsanity.com/paper/2204.13775/) (2022)
- [LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding](https://mlsanity.com/paper/2507.02843/) (2025)
- [Text Rationalization for Robust Causal Effect Estimation](https://mlsanity.com/paper/2512.05373/) (2025)
- [LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals](https://mlsanity.com/paper/2601.10700/) (2026)
- [Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community](https://mlsanity.com/paper/2110.07938/) (2021)
- [IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model](https://mlsanity.com/paper/2209.03891/) (2022)
- [Reproducibility is not construct validity: LLM measurement of institutionally situated communication](https://mlsanity.com/paper/2609.19866/) (2026)
- [What do writing features tell us about AI papers?](https://mlsanity.com/paper/2107.06310/) (2021)
- [Causality-Driven Neural Network Repair: Challenges and Opportunities](https://mlsanity.com/paper/2504.17946/) (2025)
- [Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records](https://mlsanity.com/paper/2507.20993/) (2025)

## Abstract (arXiv)

Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.