# Towards Generalizing Inferences from Trials to Target Populations

arXiv: https://arxiv.org/abs/2402.17042 · HTML: https://mlsanity.com/paper/2402.17042/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 21 of 21)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Learning Models with Uniform Performance via Distributionally Robust Optimization](https://mlsanity.com/paper/1810.08750/) (2018)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [Distributionally Robust Optimization and Generalization in Kernel Methods](https://mlsanity.com/paper/1905.10943/) (2019)
- [Causal inference methods for combining randomized trials and observational studies: a review](https://mlsanity.com/paper/2011.08047/) (2020)
- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)
- [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)
- [Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity](https://mlsanity.com/paper/2302.13428/) (2023)
- [Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator](https://mlsanity.com/paper/2307.04304/) (2023)
- [Who Are We Missing? A Principled Approach to Characterizing the Underrepresented Population](https://mlsanity.com/paper/2401.14512/) (2024)
- [Sensitivity Analysis in the Generalization of Experimental Results](https://mlsanity.com/paper/2202.03408/) (2022)
- [Designing Randomized Experiments to Predict Unit-Specific Treatment Effects](https://mlsanity.com/paper/2310.18500/) (2023)
- [Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials](https://mlsanity.com/paper/2103.04907/) (2021)
- [Worth Weighting? How to Think About and Use Weights in Survey Experiments](https://mlsanity.com/paper/1703.06808/) (2017)
- [Evaluating Ex Ante Counterfactual Predictions Using Ex Post Causal Inference](https://mlsanity.com/paper/1806.07016/) (2018)
- [Regression-based estimation of heterogeneous treatment effects when extending inferences from a randomized trial to a target population](https://mlsanity.com/paper/2110.00107/) (2021)
- [A novel decomposition to explain heterogeneity in observational and randomized studies of causality](https://mlsanity.com/paper/2208.05543/) (2022)
- [Generalizing and transporting inferences about the effects of treatment assignment subject to non-adherence](https://mlsanity.com/paper/2211.04876/) (2022)
- [A Two-Stage Method for Extending Inferences from a Collection of Trials](https://mlsanity.com/paper/2309.03693/) (2023)
- [Synthesis estimators for positivity violations with a continuous covariate](https://mlsanity.com/paper/2311.09388/) (2023)

## Built on by (most cited first, 4 of 4)

- [Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025](https://mlsanity.com/paper/2510.15217/) (2025)
- [A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference](https://mlsanity.com/paper/2505.11014/) (2025)
- [TEA-Time: Transporting Effects Across Time](https://mlsanity.com/paper/2603.07018/) (2026)
- [Mind the Sim-to-Real Gap & Think Like a Scientist](https://mlsanity.com/paper/2605.21458/) (2026)

## Abstract (arXiv)

Randomized Controlled Trials (RCTs) are pivotal in generating internally valid estimates with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing causal inference methods. However, extending these findings beyond the experimental cohort to achieve externally valid estimates is crucial for broader scientific inquiry. This paper delves into the forefront of addressing these external validity challenges, encapsulating the essence of a multidisciplinary workshop held at the Institute for Computational and Experimental Research in Mathematics (ICERM), Brown University, in Fall 2023. The workshop congregated experts from diverse fields including social science, medicine, public health, statistics, computer science, and education, to tackle the unique obstacles each discipline faces in extrapolating experimental findings. Our study presents three key contributions: we integrate ongoing efforts, highlighting methodological synergies across fields; provide an exhaustive review of generalizability and transportability based on the workshop's discourse; and identify persistent hurdles while suggesting avenues for future research. By doing so, this paper aims to enhance the collective understanding of the generalizability and transportability of causal effects, fostering cross-disciplinary collaboration and offering valuable insights for researchers working on refining and applying causal inference methods.