NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy
This addresses the need for improved statistical power in large-scale data science applications where hierarchical covariate structures exist, representing an incremental advance over prior methods.
The paper tackled the problem of controlling false discovery rate (FDR) in multiple hypothesis testing by incorporating hierarchical information among test-level covariates, which existing methods ignore, and showed that NeurT-FDR makes substantially more discoveries in synthetic and real datasets.
Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring possible hierarchy among the covariates. This strategy may not be optimal for complex large-scale problems, where hierarchical information often exists among those test-level covariates. We propose NeurT-FDR which boosts statistical power and controls FDR for multiple hypothesis testing while leveraging the hierarchy among test-level covariates. Our method parametrizes the test-level covariates as a neural network and adjusts the feature hierarchy through a regression framework, which enables flexible handling of high-dimensional features as well as efficient end-to-end optimization. We show that NeurT-FDR has strong FDR guarantees and makes substantially more discoveries in synthetic and real datasets compared to competitive baselines.