Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies
This work addresses the challenge of generalizable discoveries in integrative analysis for fields like genetics, though it is incremental as it builds on existing maximin effect theory.
The paper tackles the problem of identifying stable associations across multiple studies with heterogeneous data by modeling them with high-dimensional regressions and making inferences for maximin effects, demonstrating that genetic variants with significant maximin effects have generalizable effects under new environments using yeast growth data.
Integrative analysis of data from multiple sources is critical to making generalizable discoveries. Associations that are consistently observed across multiple source populations are more likely to be generalized to target populations with possible distributional shifts. In this paper, we model the heterogeneous multi-source data with multiple high-dimensional regressions and make inferences for the maximin effect (Meinshausen, B{ü}hlmann, AoS, 43(4), 1801--1830). The maximin effect provides a measure of stable associations across multi-source data. A significant maximin effect indicates that a variable has commonly shared effects across multiple source populations, and these shared effects may be generalized to a broader set of target populations. There are challenges associated with inferring maximin effects because its point estimator can have a non-standard limiting distribution. We devise a novel sampling method to construct valid confidence intervals for maximin effects. The proposed confidence interval attains a parametric length. This sampling procedure and the related theoretical analysis are of independent interest for solving other non-standard inference problems. Using genetic data on yeast growth in multiple environments, we demonstrate that the genetic variants with significant maximin effects have generalizable effects under new environments.