# A Review of Generalizability and Transportability

arXiv: https://arxiv.org/abs/2102.11904 · HTML: https://mlsanity.com/paper/2102.11904/

## 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 (most cited first, 18 of 18)

- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [External Validity: From Do-Calculus to Transportability Across Populations](https://mlsanity.com/paper/1503.01603/) (2015)
- [Removing Hidden Confounding by Experimental Grounding](https://mlsanity.com/paper/1810.11646/) (2018)
- [Learning Weighted Representations for Generalization Across Designs](https://mlsanity.com/paper/1802.08598/) (2018)
- [Combining Observational and Experimental Datasets Using Shrinkage Estimators](https://mlsanity.com/paper/2002.06708/) (2020)
- [Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals](https://mlsanity.com/paper/1709.04589/) (2017)
- [Efficient and robust methods for causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target population](https://mlsanity.com/paper/1908.09230/) (2019)
- [Randomization Inference for Treatment Effect Variation](https://mlsanity.com/paper/1412.5000/) (2014)
- [Propensity Score Methods for Merging Observational and Experimental Datasets](https://mlsanity.com/paper/1804.07863/) (2018)
- [An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functions](https://mlsanity.com/paper/1510.04195/) (2015)
- [Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects](https://mlsanity.com/paper/1610.06943/) (2016)
- [Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population](https://mlsanity.com/paper/1905.10684/) (2019)
- [Sensitivity 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 details](https://mlsanity.com/paper/2011.13058/) (2020)
- [Combining Non-probability and Probability Survey Samples Through Mass Imputation](https://mlsanity.com/paper/1812.10694/) (2018)
- [Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?](https://mlsanity.com/paper/1702.03767/) (2017)
- [On variance estimation for generalizing from a trial to a target population](https://mlsanity.com/paper/1704.07789/) (2017)
- [Generalizing trial findings using nested trial designs with sub-sampling of non-randomized individuals](https://mlsanity.com/paper/1902.06080/) (2019)
- [Transporting Experimental Results with Entropy Balancing](https://mlsanity.com/paper/2002.07899/) (2020)

## Built on by (most cited first, 30 of 37)

- [A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources](https://mlsanity.com/paper/2103.06261/) (2021)
- [Falsification before Extrapolation in Causal Effect Estimation](https://mlsanity.com/paper/2209.13708/) (2022)
- [Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictions](https://mlsanity.com/paper/2301.13133/) (2023)
- [Why Do Probabilistic Clinical Models Fail To Transport Between Sites?](https://mlsanity.com/paper/2311.04787/) (2023)
- [Hidden yet quantifiable: A lower bound for confounding strength using randomized trials](https://mlsanity.com/paper/2312.03871/) (2023)
- [Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population](https://mlsanity.com/paper/2401.14512/) (2024)
- [Combining Experimental and Historical Data for Policy Evaluation](https://mlsanity.com/paper/2406.00317/) (2024)
- [Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data](https://mlsanity.com/paper/2301.05491/) (2023)
- [A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty](https://mlsanity.com/paper/2406.02584/) (2024)
- [Out-of-Variable Generalization for Discriminative Models](https://mlsanity.com/paper/2304.07896/) (2023)
- [Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning](https://mlsanity.com/paper/2306.01086/) (2023)
- [Principles from Clinical Research for NLP Model Generalization](https://mlsanity.com/paper/2311.03663/) (2023)
- [Towards Generalizing Inferences from Trials to Target Populations](https://mlsanity.com/paper/2402.17042/) (2024)
- [Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization](https://mlsanity.com/paper/2412.08869/) (2024)
- [Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025](https://mlsanity.com/paper/2510.15217/) (2025)
- [Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaid](https://mlsanity.com/paper/2109.13288/) (2021)
- [Towards Representation Learning for Weighting Problems in Design-Based Causal Inference](https://mlsanity.com/paper/2409.16407/) (2024)
- [Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics](https://mlsanity.com/paper/2204.09291/) (2022)
- [A Causal Inference Framework for Leveraging External Controls in Hybrid Trials](https://mlsanity.com/paper/2305.08969/) (2023)
- [Regularizing Extrapolation in Causal Inference](https://mlsanity.com/paper/2509.17180/) (2025)
- [Observationally Informed Adaptive Causal Experimental Design](https://mlsanity.com/paper/2603.03785/) (2026)
- [Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment](https://mlsanity.com/paper/2603.19186/) (2026)
- [When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels](https://mlsanity.com/paper/2609.13148/) (2026)
- [Considerations Across Three Cultures: Parametric Regressions, Interpretable Algorithms, and Complex Algorithms](https://mlsanity.com/paper/2104.06571/) (2021)
- [Generalizing experimental findings: identification beyond adjustments](https://mlsanity.com/paper/2206.06699/) (2022)
- [Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption](https://mlsanity.com/paper/2309.16409/) (2023)
- [Externally Valid Policy Evaluation Combining Trial and Observational Data](https://mlsanity.com/paper/2310.14763/) (2023)
- [Towards a Transportable Causal Network Model Based on Observational Healthcare Data](https://mlsanity.com/paper/2311.08427/) (2023)
- [A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference](https://mlsanity.com/paper/2505.11014/) (2025)
- [Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts](https://mlsanity.com/paper/2505.24427/) (2025)

## 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.