# How to Make Causal Inferences Using Texts

arXiv: https://arxiv.org/abs/1802.02163 · HTML: https://mlsanity.com/paper/1802.02163/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2018.

## Built on by (most cited first, 30 of 59)

- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [A Causal Lens for Controllable Text Generation](https://mlsanity.com/paper/2201.09119/) (2022)
- [Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models](https://mlsanity.com/paper/2306.04746/) (2023)
- [Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labels](https://mlsanity.com/paper/2406.17633/) (2024)
- [Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP](https://mlsanity.com/paper/2110.03618/) (2021)
- [End-To-End Causal Effect Estimation from Unstructured Natural Language Data](https://mlsanity.com/paper/2407.07018/) (2024)
- [Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AI](https://mlsanity.com/paper/2409.09467/) (2024)
- [Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments](https://mlsanity.com/paper/2410.00903/) (2024)
- [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)
- [Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies](https://mlsanity.com/paper/2203.09672/) (2022)
- [DoubleMLDeep: Estimation of Causal Effects with Multimodal Data](https://mlsanity.com/paper/2402.01785/) (2024)
- [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)
- [Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities](https://mlsanity.com/paper/2301.12985/) (2023)
- [Causal Inference on Outcomes Learned from Text](https://mlsanity.com/paper/2503.00725/) (2025)
- [Proximal Causal Inference With Text Data](https://mlsanity.com/paper/2401.06687/) (2024)
- [AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments](https://mlsanity.com/paper/2410.08917/) (2024)
- [Leveraging text data for causal inference using electronic health records](https://mlsanity.com/paper/2307.03687/) (2023)
- [Text-Transport: Toward Learning Causal Effects of Natural Language](https://mlsanity.com/paper/2310.20697/) (2023)
- [Deep Autoregressive Models as Causal Inference Engines](https://mlsanity.com/paper/2409.18581/) (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)
- [A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms](https://mlsanity.com/paper/2209.06367/) (2022)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [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)
- [A Bayesian approach to modeling topic-metadata relationships](https://mlsanity.com/paper/2104.02496/) (2021)
- [Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Texts](https://mlsanity.com/paper/2202.00848/) (2022)
- [Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension](https://mlsanity.com/paper/2311.10344/) (2023)

## Abstract (arXiv)

New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collections of text. We introduce a conceptual framework for making causal inferences with discovered measures as a treatment or outcome. Our framework enables researchers to discover high-dimensional textual interventions and estimate the ways that observed treatments affect text-based outcomes. We argue that nearly all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation. But estimating this latent representation, we show, creates new risks: we may introduce an identification problem or overfit. To address these risks we describe a split-sample framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic response. Our work provides a rigorous foundation for text-based causal inferences.