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New MAS method controls false discovery rate in scientific research

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new method called Model-Agnostic Selection with agent-X importance (MAS) to improve covariate selection in scientific discovery. This approach aims to control the false discovery rate (FDR) without making specific assumptions about the covariate distribution, unlike traditional methods. MAS utilizes a sample-splitting strategy and a novel variable importance measure, which is defined by comparing predictive performance on original and modified covariate distributions. The method includes theoretical guarantees for FDR control and has demonstrated competitive performance in simulations and real-world analyses. AI

RANK_REASON The cluster contains a research paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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New MAS method controls false discovery rate in scientific research

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The cluster contains a research paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Mengtao Wen, Haojie Ren, Changliang Zou ·

    Controlling the False Discovery Rate via Model-Agnostic Importance Measures

    arXiv:2610.21360v1 Announce Type: cross 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 specif…

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