A note on semiparametric efficient generalization of causal effects from randomized trials to target populations

Journal Article (Journal Article)

When effect modifiers influence the decision to participate in randomized trials, generalizing causal effect estimates to an external target population requires the knowledge of two scores–the propensity score for receiving treatment and the sampling score for trial participation. While the former score is known due to randomization, the latter score is usually unknown and estimated from data. Under unconfounded trial participation, we characterize the asymptotic efficiency bounds for estimating two causal estimands–the population average treatment effect and the average treatment effect among the non-participants–and examine the role of the scores. We also study semiparametric efficient estimators that directly balance the weighted trial sample toward the target population, and illustrate their operating characteristics via simulations.

Full Text

Duke Authors

Cited Authors

  • Li, F; Hong, H; Stuart, EA

Published Date

  • January 1, 2021

Published In

Electronic International Standard Serial Number (EISSN)

  • 1532-415X

International Standard Serial Number (ISSN)

  • 0361-0926

Digital Object Identifier (DOI)

  • 10.1080/03610926.2021.2020291

Citation Source

  • Scopus