mlsanityData to 7 Oct 2026

Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals

arXiv 1709.04589 ↗First posted 14 Sep 2017stat.MEcited by 24

Not on the map. Why?

Abstract (arXiv)

We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 2; 2023: 5; 2024: 7; 2025: 0; 2026: 6.

24 papers.

Sort:

By area:

  1. 2021A Review of Generalizability and Transportabilitycited by 36
  2. 2021Generalizing Off-Policy Learning under Sample Selection Biascited by 16
  3. 2021A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sourcescited by 14
  4. 2024Prediction-powered Generalization of Causal Inferencescited by 14
  5. 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effectscited by 13
  6. 2021Intact-VAE: Estimating Treatment Effects under Unobserved Confoundingcited by 10
  7. 2023Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictionscited by 10
  8. 2023Offline Minimax Soft-Q-learning Under Realizability and Partial Coveragecited by 9
  9. 2024Adaptive-TMLE for the Average Treatment Effect based on Randomized Controlled Trial Augmented with Real-World Datacited by 7
  10. 2023Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival datacited by 5
  11. 2024Benchmarking Observational Studies with Experimental Data under Right-Censoringcited by 4
  12. 2024Towards Generalizing Inferences from Trials to Target Populationscited by 3
12 more
  1. 2024Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalizationcited by 3
  2. 2022Targeted Optimal Treatment Regime Learning Using Summary Statisticscited by 2
  3. 2024Continuous Treatment Effects with Surrogate Outcomescited by 1
  4. 2022Generalizing experimental findings: identification beyond adjustmentscited by 0
  5. 2023Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumptioncited by 0
  6. 2024Testing Generalizability in Causal Inferencecited by 0
  7. 2026TEA-Time: Transporting Effects Across Timecited by 0
  8. 2026Transfer Learning for Meta-analysis Under Covariate Shiftcited by 0
  9. 2026Bayesian fusion forests for heterogeneous treatment effects on survival from randomised and real-world datacited by 0
  10. 2026Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shiftscited by 0
  11. 2026Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shiftcited by 0
  12. 2026Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimationcited by 0