Robust Sample Weighting to Facilitate Individualized Treatment Rule Learning for a Target Population

Abstract

Learning individualized treatment rules (ITRs) is an important topic in precision medicine. Current literature mainly focuses on deriving ITRs from a single source population. We consider the observational data setting when the source population differs from a target population of interest. We assume subject covariates are available from both populations, but treatment and outcome data are only available from the source population. Although adjusting for differences between source and target populations can potentially lead to an improved ITR for the target population, it can substantially increase the variability in ITR estimation. To address this dilemma, we develop a weighting framework that aims to tailor an ITR for a given target population and protect against high variability due to superfluous covariate shift adjustments. Our method seeks covariate balance over a nonparametric function class characterized by a reproducing kernel Hilbert space and can improve many ITR learning methods that rely on weights. We show that the proposed method encompasses importance weights and the so-called overlap weights as two extreme cases, allowing for a better bias-variance trade-off in between. Numerical examples demonstrate that the use of our weighting method can greatly improve ITR estimation for the target population compared with other weighting methods.

Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap

“…I illustrate these issues by considering a very simple setting where the propensity scores are known and a linear model describes heterogeneity in average causal effects. This particular setting is designed to help us focus on the core issues related to limited overlap, paving 6 See Ai et al (2021); Athey et al (2018); Ben-Michael and Keele (2022); Chen et al (2021); Graham et al (2012); Hainmueller (2012); Hirshberg and Wager (2021); Ratkovic (2014, 2015); Khan and Ugander (2021); Li (2019); Li et al (2018); Matsouaka et al (2022); Ning et al (2020); ; Wang and Shah (2020); Wang and Zubizarreta (2020); Wong and Chan (2018); Yang and Ding (2018); Zhao (2019); Zubizarreta (2015). Some methods, such as those of Chen et al (2008) and Hirshberg and Wager (2021), make weaker assumptions than strong overlap but still restrict limited overlap.…”