mlsanityData to 14 Oct 2026

Leveraging text data for causal inference using electronic health records

arXiv 2307.03687 ↗First posted 9 Jun 2023cs.CL stat.AP stat.MEcited by 6

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Abstract (arXiv)

In studies that rely on data from electronic health records (EHRs), unstructured text data such as clinical progress notes offer a rich source of information about patient characteristics and care that may be missing from structured data. Despite the prevalence of text in clinical research, these data are often ignored for the purposes of quantitative analysis due their complexity. This paper presents a unified framework for leveraging text data to support causal inference with electronic health data at multiple stages of analysis. In particular, we consider how natural language processing and statistical text analysis can be combined with standard inferential techniques to address common challenges due to missing data, confounding bias, and treatment effect heterogeneity. Through an application to a recent EHR study investigating the effects of a non-randomized medical intervention on patient outcomes, we show how incorporating text data in a traditional matching analysis can help strengthen the validity of an estimated treatment effect and identify patient subgroups that may benefit most from treatment. We believe these methods have the potential to expand the scope of secondary analysis of clinical data to domains where structured EHR data is limited, such as in developing countries. To this end, we provide code and open-source replication materials to encourage adoption and broader exploration of these techniques in clinical research.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 10 of its 77 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

10 of 77 references.

References · 10

  1. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  2. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 493
  3. 2018Scalable and accurate deep learning for electronic health recordscited by 280
  4. 2018How to Make Causal Inferences Using Textscited by 59
  5. 2020Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimatescited by 59
  6. 2017Some methods for heterogeneous treatment effect estimation in high-dimensionscited by 48
  7. 2018Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Qualitycited by 21
  8. 2019A stacked approach for chained equations multiple imputation incorporating the substantive modelcited by 2
  9. 2022A comparison of strategies for selecting auxiliary variables for multiple imputationcited by 2
  10. 2017Insights on Variance Estimation for Blocked and Matched Pairs Designscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

  1. 2024Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatmentscited by 16
  2. 2025A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?cited by 3
  3. 2026LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategiescited by 1
  4. 2026Causal Inference with Unstructured Treatmentscited by 1
  5. 2026The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Textcited by 0
  6. 2026Information Set Emulation: Causal Certificates for AI Derived EHR Featurescited by 0