Compositional Covariate Importance Testing via Partial Conjunction of Bivariate Hypotheses

TL;DR

This paper introduces a new framework for testing covariate importance in regression models with compositional data, overcoming the challenge that traditional methods are powerless due to the sum constraint.

Contribution

It develops novel hypothesis tests and variable selection procedures based on bivariate conditional independence and partial conjunction testing, with theoretical guarantees and empirical validation.

Findings

Methods are valid and control false positives.

Proposed tests are powerful across various scenarios.

Framework addresses compositional data challenges effectively.

Abstract

Compositional data (i.e., data comprising random variables that sum up to a constant) arises in many applications including microbiome studies, chemical ecology, political science, and experimental designs. Yet when compositional data serve as covariates in a regression, the sum constraint renders every covariate automatically conditionally independent of the response given the other covariates, since each covariate is a deterministic function of the others. Since essentially all covariate importance tests and variable selection methods, including parametric ones, are at their core testing conditional independence, they are all completely powerless on regression problems with compositional covariates. In fact, compositionality causes ambiguity in the very notion of relevant covariates. To address this problem, we identify a natural way to translate the typical notion of relevant covariates to the setting with compositional covariates and establish that it is intuitive, well-defined, and unique. We then develop corresponding hypothesis tests and controlled variable selection procedures via a novel connection with \emph{bivariate} conditional independence testing and partial conjunction hypothesis testing. Finally, we provide theoretical guarantees of the validity of our methods, and through numerical experiments demonstrate that our methods are not only valid but also powerful across a range of data-generating scenarios.