# On variance estimation for generalizing from a trial to a target population

arXiv: https://arxiv.org/abs/1704.07789 · HTML: https://mlsanity.com/paper/1704.07789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)

## Abstract (arXiv)

Randomized controlled trials (RCTs) provide strong internal validity compared with observational studies. However, selection bias threatens the external validity of randomized trials. Thus, RCT results may not apply to either broad public policy populations or narrow populations, such as specific insurance pools. Some researchers use propensity scores (PSs) to generalize results from an RCT to a target population. In this scenario, a PS is defined as the probability of participating in the trial conditioning on observed covariates. We study a model-free inverse probability weighted estimator (IPWE) of the average treatment effect in a target population with data from a randomized trial. We present variance estimators and compare the performance of our method with that of model-based approaches. We examine the robustness of the model-free estimators to heterogeneous treatment effects.