mlsanityData to 7 Oct 2026

How to Make Causal Inferences Using Texts

arXiv 1802.02163 ↗First posted 6 Feb 2018stat.ML cs.CL stat.MEcited by 59

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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.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2018.

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Citing papers per year: 2021: 9; 2022: 6; 2023: 7; 2024: 16; 2025: 14; 2026: 7.

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  1. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 177
  2. 2022A Causal Lens for Controllable Text Generationcited by 45
  3. 2023Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Modelscited by 37
  4. 2024Knowledge Distillation in Automated Annotation: Supervised Text Classification with LLM-Generated Training Labelscited by 25
  5. 2021Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLPcited by 21
  6. 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Datacited by 18
  7. 2024Keeping Humans in the Loop: Human-Centered Automated Annotation with Generative AIcited by 16
  8. 2024Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatmentscited by 16
  9. 2022Causal Estimation for Text Data with (Apparent) Overlap Violationscited by 15
  10. 2021Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysiscited by 14
  11. 2022Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxiescited by 13
  12. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
38 more
  1. 2021Generating Synthetic Text Data to Evaluate Causal Inference Methodscited by 9
  2. 2023Integrating Earth Observation Data into Causal Inference: Challenges and Opportunitiescited by 8
  3. 2024DoubleMLDeep: Estimation of Causal Effects with Multimodal Datacited by 8
  4. 2024Proximal Causal Inference With Text Datacited by 7
  5. 2024AutoPersuade: A Framework for Evaluating and Explaining Persuasive Argumentscited by 7
  6. 2023Text-Transport: Toward Learning Causal Effects of Natural Languagecited by 6
  7. 2024Deep Autoregressive Models as Causal Inference Enginescited by 6
  8. 2025Causal Inference on Outcomes Learned from Textcited by 6
  9. 2021CausalNLP: A Practical Toolkit for Causal Inference with Textcited by 5
  10. 2023Leveraging text data for causal inference using electronic health recordscited by 5
  11. 2023Causal Inference from Text: Unveiling Interactions between Variablescited by 5
  12. 2021Bayesian Topic Regression for Causal Inferencecited by 4
  13. 2022A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigmscited by 4
  14. 2024Estimating Causal Effects of Text Interventions Leveraging LLMscited by 4
  15. 2021Text as Causal Mediators: Research Design for Causal Estimates of Differential Treatment of Social Groups via Language Aspectscited by 3
  16. 2021A Bayesian approach to modeling topic-metadata relationshipscited by 2
  17. 2022Some Reflections on Drawing Causal Inference using Textual Data: Parallels Between Human Subjects and Organized Textscited by 2
  18. 2023Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehensioncited by 2
  19. 2024Isolated Causal Effects of Natural Languagecited by 2
  20. 2025LLM-Driven Treatment Effect Estimation Under Inference Time Text Confoundingcited by 2
  21. 2023Natural Language Processing for Policymakingcited by 1
  22. 2024Crowdsourced Adaptive Surveyscited by 1
  23. 2024Causal Inference for Human-Language Model Collaborationcited by 1
  24. 2024Alignment Helps Make the Most of Multimodal Datacited by 1
  25. 2025An Empirical Study of Causal Relation Extraction Transfer: Design and Datacited by 1
  26. 2025Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952-2012cited by 1
  27. 2025Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventionscited by 1
  28. 2025E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Timecited by 1
  29. 2025Leveraging semantic similarity for experimentation with AI-generated treatmentscited by 1
  30. 2025Text Rationalization for Robust Causal Effect Estimationcited by 1
  31. 2026The Proxy Presumption: From Semantic Embeddings to Valid Social Measurescited by 1
  32. 2021What do writing features tell us about AI papers?cited by 0
  33. 2024Discovering influential text using convolutional neural networkscited by 0
  34. 2024Structuring Quantitative Image Analysis with Object Prominencecited by 0
  35. 2024Modeling Story Expectations: A Generative Framework using LLMscited by 0
  36. 2025Policy Learning with a Natural Language Action Space: A Causal Approachcited by 0
  37. 2025Testing Hypotheses of Covariate Effects on Topics of Discoursecited by 0
  38. 2025Leveraging Generative Artificial Intelligence for Causal Inference with Unstructured Datacited by 0

50 most cited of 59 All 59 (JSON)