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

05/03/2021
by   Rui Chen, et al.
0

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.

READ FULL TEXT
01/17/2022

Targeted Optimal Treatment Regime Learning Using Summary Statistics

Personalized decision-making, aiming to derive optimal individualized tr...
03/16/2022

One-step weighting to generalize and transport treatment effect estimates to a target population

Weighting methods are often used to generalize and transport estimates o...
11/02/2021

Leveraging Population Outcomes to Improve the Generalization of Experimental Results

Generalizing causal estimates in randomized experiments to a broader tar...
06/27/2021

A Generalizability Score for Aggregate Causal Effect

Scientists frequently generalize population level causal quantities such...
01/11/2019

Minimax Linear Estimation of the Retargeted Mean

Weighting methods that adjust for observed covariates, such as inverse p...
05/11/2022

Externally Valid Treatment Choice

We consider the problem of learning treatment (or policy) rules that are...
07/01/2021

Mandoline: Model Evaluation under Distribution Shift

Machine learning models are often deployed in different settings than th...