Controlling the False Discovery Rate via Model-Agnostic Importance Measures
Abstract
Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models. Traditional variable selection methods often rely on specific model assumptions, while Model-X knockoffs provide a general way that leverages knowledge of the covariate distribution to produce a variable selection algorithm. This study puts forward a complementary method to Model-X knockoffs, named Model-Agnostic Selection with agent-X importance (MAS), which utilizes the sample-splitting strategy and a novel variable importance measure to bypass distributional assumptions on covariates. The proposed model-agnostic agent-X importance is defined to evaluate the difference of oracle predictiveness on both the original distribution and the distribution with agent variable in population level, and can be efficiently estimated by constructing the agent variable as some constant and cross-fitting strategy. The MAS method integrates a resplit-per-covariate strategy to decouple estimator dependencies and a data aggregation technique to enhance power. Theoretical guarantees for asymptotic FDR control are established under mild conditions. Extensive simulations and real-data analyses demonstrate that MAS provides competitive performances with existing works in FDR control, computational efficiency and power.
Related
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BibTeX
@misc{wen2026controlling,
title = {{Controlling the False Discovery Rate via Model-Agnostic Importance Measures}},
author = {Mengtao Wen and Haojie Ren and Changliang Zou},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.21360},
eprint = {2610.21360},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.21360},
}
RIS
TY - UNPB
TI - Controlling the False Discovery Rate via Model-Agnostic Importance Measures
AU - Mengtao Wen
AU - Haojie Ren
AU - Changliang Zou
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2610.21360
UR - https://arxiv.org/abs/2610.21360
AB - Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models. Traditional variable selection methods often rely on specific model assumptions, while Model-X knockoffs provide a general way that leverages knowledge of the covariate distribution to produce a variable selection algorithm. This study puts forward a complementary method to Model-X knockoffs, named Model-Agnostic Selection with agent-X importance (MAS), which utilizes the sample-splitting strategy and a novel variable importance measure to bypass distributional assumptions on covariates. The proposed model-agnostic agent-X importance is defined to evaluate the difference of oracle predictiveness on both the original distribution and the distribution with agent variable in population level, and can be efficiently estimated by constructing the agent variable as some constant and cross-fitting strategy. The MAS method integrates a resplit-per-covariate strategy to decouple estimator dependencies and a data aggregation technique to enhance power. Theoretical guarantees for asymptotic FDR control are established under mild conditions. Extensive simulations and real-data analyses demonstrate that MAS provides competitive performances with existing works in FDR control, computational efficiency and power.
ER -