mlsanityData to 14 Oct 2026

A Review of Generalizability and Transportability

arXiv 2102.11904 ↗First posted 23 Feb 2021stat.ME stat.AP stat.MLcited by 37

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Abstract (arXiv)

When assessing causal effects, determining the target population to which the results are intended to generalize is a critical decision. Randomized and observational studies each have strengths and limitations for estimating causal effects in a target population. Estimates from randomized data may have internal validity but are often not representative of the target population. Observational data may better reflect the target population, and hence be more likely to have external validity, but are subject to potential bias due to unmeasured confounding. While much of the causal inference literature has focused on addressing internal validity bias, both internal and external validity are necessary for unbiased estimates in a target population. This paper presents a framework for addressing external validity bias, including a synthesis of approaches for generalizability and transportability, the assumptions they require, as well as tests for the heterogeneity of treatment effects and differences between study and target populations.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). Builds on 18 of its 147 references that are in the map. First posted on arXiv 23 Feb 2021.

Builds on

18 of 147 references.

References · 18

  1. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 255
  2. 2015External Validity: From Do-Calculus to Transportability Across Populationscited by 75
  3. 2018Removing Hidden Confounding by Experimental Groundingcited by 64
  4. 2018Learning Weighted Representations for Generalization Across Designscited by 41
  5. 2020Combining Observational and Experimental Datasets Using Shrinkage Estimatorscited by 28
  6. 2017Generalizing causal inferences from individuals in randomized trials to all trial-eligible individualscited by 25
  7. 2019Efficient and robust methods for causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target populationcited by 10
  8. 2014Randomization Inference for Treatment Effect Variationcited by 9
  9. 2018Propensity Score Methods for Merging Observational and Experimental Datasetscited by 9
  10. 2015An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functionscited by 8
  11. 2016Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effectscited by 5
  12. 2019Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target populationcited by 5
6 more
  1. 2020Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation detailscited by 4
  2. 2018Combining Non-probability and Probability Survey Samples Through Mass Imputationcited by 2
  3. 2017Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?cited by 1
  4. 2017On variance estimation for generalizing from a trial to a target populationcited by 1
  5. 2019Generalizing trial findings using nested trial designs with sub-sampling of non-randomized individualscited by 1
  6. 2020Transporting Experimental Results with Entropy Balancingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 11; 2024: 6; 2025: 5; 2026: 9.

37 papers.

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  1. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  2. 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
  3. 2023Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictionscited by 10
  4. 2023Why Do Probabilistic Clinical Models Fail To Transport Between Sites?cited by 9
  5. 2023Hidden yet quantifiable: A lower bound for confounding strength using randomized trialscited by 8
  6. 2024Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Populationcited by 6
  7. 2024Combining Experimental and Historical Data for Policy Evaluationcited by 6
  8. 2023Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival datacited by 5
  9. 2024A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Povertycited by 5
  10. 2023Out-of-Variable Generalization for Discriminative Modelscited by 4
  11. 2023Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learningcited by 4
  12. 2023Principles from Clinical Research for NLP Model Generalizationcited by 4
25 more
  1. 2024Towards Generalizing Inferences from Trials to Target Populationscited by 4
  2. 2024Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalizationcited by 4
  3. 2025Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025cited by 4
  4. 2021Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaidcited by 2
  5. 2024Towards Representation Learning for Weighting Problems in Design-Based Causal Inferencecited by 2
  6. 2022Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnosticscited by 1
  7. 2023A Causal Inference Framework for Leveraging External Controls in Hybrid Trialscited by 1
  8. 2025Regularizing Extrapolation in Causal Inferencecited by 1
  9. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  10. 2026Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignmentcited by 1
  11. 2026When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panelscited by 1
  12. 2021Considerations Across Three Cultures: Parametric Regressions, Interpretable Algorithms, and Complex Algorithmscited by 0
  13. 2022Generalizing experimental findings: identification beyond adjustmentscited by 0
  14. 2023Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumptioncited by 0
  15. 2023Externally Valid Policy Evaluation Combining Trial and Observational Datacited by 0
  16. 2023Towards a Transportable Causal Network Model Based on Observational Healthcare Datacited by 0
  17. 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inferencecited by 0
  18. 2025Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Postscited by 0
  19. 2025Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learningcited by 0
  20. 2026The Illusion of AI Expertise Under Uncertainty: A Probabilistic Paradigm to Interpret Results Under Ground Truth Variationcited by 0
  21. 2026TEA-Time: Transporting Effects Across Timecited by 0
  22. 2026Nonparametric Regression Discontinuity Designs with Survival Outcomescited by 0
  23. 2026Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confoundingcited by 0
  24. 2026Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shiftscited by 0
  25. 2026Causal Generalization of Continuous Treatment Effects under Covariate Shiftcited by 0