Universal adaptability: Target-independent inference that competes with propensity scoring.

ABSTRACT: The gold-standard approaches for gleaning statistically valid conclusions from data involve random sampling from the population. Collecting properly randomized data, however, can be challenging, so modern statistical methods, including propensity score reweighting, aim to enable valid inferences when random sampling is not feasible. We put forth an approach for making inferences based on available data from a source population that may differ in composition in unknown ways from an eventual target population. Whereas propensity scoring requires a separate estimation procedure for each different target population, we show how to build a single estimator, based on source data alone, that allows for efficient and accurate estimates on any downstream target data. We demonstrate, theoretically a

SUBMITTER: Kim MP 

PROVIDER: S-EPMC8794832 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature