mlsanityData to 14 Oct 2026

Causal inference methods for combining randomized trials and observational studies: a review

arXiv 2011.08047 ↗First posted 16 Nov 2020stat.MEcited by 51

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Abstract (arXiv)

With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the effect of the treatment from that of unwanted (confounding) co-occurring effects but they may suffer from unrepresentativeness, and thus lack external validity. On the other hand, large observational samples are often more representative of the target population but can conflate confounding effects with the treatment of interest. In this paper, we review the growing literature on methods for causal inference on combined RCTs and observational studies, striving for the best of both worlds. We first discuss identification and estimation methods that improve generalizability of RCTs using the representativeness of observational data. Classical estimators include weighting, difference between conditional outcome models, and doubly robust estimators. We then discuss methods that combine RCTs and observational data to either ensure uncounfoundedness of the observational analysis or to improve (conditional) average treatment effect estimation. We also connect and contrast works developed in both the potential outcomes literature and the structural causal model literature. Finally, we compare the main methods using a simulation study and real world data to analyze the effect of tranexamic acid on the mortality rate in major trauma patients. A review of available codes and new implementations is also provided.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Nov 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 3; 2023: 5; 2024: 13; 2025: 16; 2026: 9.

51 papers.

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  1. 2024Causal machine learning for predicting treatment outcomescited by 74
  2. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  3. 2024Prediction-powered Generalization of Causal Inferencescited by 15
  4. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  5. 2021Multi-Source Causal Inference Using Control Variatescited by 13
  6. 2021Causal Effect Variational Autoencoder with Uniform Treatmentcited by 8
  7. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  8. 2024Combining Experimental and Historical Data for Policy Evaluationcited by 6
  9. 2024Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasetscited by 6
  10. 2021Deep Treatment-Adaptive Network for Causal Inferencecited by 5
  11. 2023Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival datacited by 5
  12. 2025Representation Retrieval Learning for Heterogeneous Data Integrationcited by 5
38 more
  1. 2025Doubly robust estimation of causal effects for random object outcomes with continuous treatmentscited by 5
  2. 2023Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learningcited by 4
  3. 2024Benchmarking Observational Studies with Experimental Data under Right-Censoringcited by 4
  4. 2024Towards Generalizing Inferences from Trials to Target Populationscited by 4
  5. 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysiscited by 4
  6. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  7. 2024Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalizationcited by 4
  8. 2025The Hardness of Validating Observational Studies with Experimental Datacited by 4
  9. 2025Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025cited by 4
  10. 2021Robust Sample Weighting to Facilitate Individualized Treatment Rule Learning for a Target Populationcited by 3
  11. 2023Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimatorscited by 3
  12. 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanismscited by 3
  13. 2024Meta-Analysis with Untrusted Datacited by 2
  14. 2024Estimating Joint Interventional Distributions from Marginal Interventional Datacited by 2
  15. 2025Clustering and Pruning in Causal Data Fusioncited by 2
  16. 2025Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregationcited by 2
  17. 2025Probabilistic Modelling is Sufficient for Causal Inferencecited by 2
  18. 2026The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choicecited by 2
  19. 2022Robust Direct Learning for Causal Data Fusioncited by 1
  20. 2024Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effectscited by 1
  21. 2025Multi-task Learning for Heterogeneous Multi-source Block-Wise Missing Datacited by 1
  22. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  23. 2026Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignmentcited by 1
  24. 2022Generalizing experimental findings: identification beyond adjustmentscited by 0
  25. 2023Externally Valid Policy Evaluation Combining Trial and Observational Datacited by 0
  26. 2024Accounting for Missing Covariates in Heterogeneous Treatment Estimationcited by 0
  27. 2025Optimal Policy Adaptation under Covariate Shiftcited by 0
  28. 2025A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confoundingcited by 0
  29. 2025Uncovering Bias Mechanisms in Observational Studiescited by 0
  30. 2025Conditional Average Treatment Effect Estimation Under Hidden Confounderscited by 0
  31. 2025Augmenting Limited and Biased RCTs through Pseudo-Sample Matching-Based Observational Data Fusion Methodcited by 0
  32. 2025Transferring Causal Effects using Proxiescited by 0
  33. 2025Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesiscited by 0
  34. 2026Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studiescited by 0
  35. 2026Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effectscited by 0
  36. 2026Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confoundingcited by 0
  37. 2026Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLEcited by 0
  38. 2026An Introduction to Causal Reinforcement Learningcited by 0

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