On the power of conditional independence testing under model-X

DOI10.1214/22-EJS2085MaRDI QIDQ2106800

Aaditya Ramdas, Eugene Katsevich

Publication date: 19 December 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.05506

zbMATH Keywords

conditional inferencepower analysisNeyman-Pearsonconditional independence testingmodel-X

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

Related Items (5)

Detecting distributional differences in labeled sequence data with application to tropical cyclone satellite imagery ⋮ Learning to increase the power of conditional randomization tests ⋮ Dimension-agnostic inference using cross U-statistics ⋮ The Lasso with general Gaussian designs with applications to hypothesis testing ⋮ What is a Randomization Test?

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Predictive inference with the jackknife+
• The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
• Higher order inference on a treatment effect under low regularity conditions
• Controlling the false discovery rate via knockoffs
• A note on the measurability of convex sets
• Targeted learning in data science. Causal inference for complex longitudinal studies
• A paradox from randomization-based causal inference
• Covariance adjustment in radomized experiments and observational studies
• Testing goodness-of-fit and conditional independence with approximate co-sufficient sampling
• Robust inference with knockoffs
• Relaxing the assumptions of knockoffs by conditioning
• Covariate-adjusted Fisher randomization tests for the average treatment effect
• Multi-center clinical trials: randomization and ancillary statistics
• Classification accuracy as a proxy for two-sample testing
• RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs
• The central role of the propensity score in observational studies for causal effects
• Root-N-Consistent Semiparametric Regression
• Estimating Exposure Effects by Modelling the Expectation of Exposure Conditional on Confounders
• Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
• AdaPT: An Interactive Procedure for Multiple Testing with Side Information
• Causal inference in genetic trio studies
• Fast and powerful conditional randomization testing via distillation
• The Holdout Randomization Test for Feature Selection in Black Box Models
• Double/debiased machine learning for treatment and structural parameters
• A high-dimensional power analysis of the conditional randomization test and knockoffs
• The Conditional Permutation Test for Independence While Controlling for Confounders
• Deep Knockoffs
• Causal Inference for Statistics, Social, and Biomedical Sciences
• The LASSO Risk for Gaussian Matrices
• Non-parametric Methods for Doubly Robust Estimation of Continuous Treatment Effects
• Testing Statistical Hypotheses
• Gene hunting with hidden Markov model knockoffs
• Causal Inference With General Treatment Regimes
• Randomization Tests for Weak Null Hypotheses in Randomized Experiments
• Doubly robust tests of exposure effects under high‐dimensional confounding

This page was built for publication: On the power of conditional independence testing under model-X