On the Hardness of Conditional Independence Testing In Practice

NeurIPSSpotlight2025

Authors: Zheng He, Roman Pogodin, Yazhe Li, Namrata Deka, Arthur Gretton, Danica J. Sutherland
Venue: NeurIPS 2025
Track: Spotlight

TL;DR

Tests of conditional independence (CI) underpin a number of important problems in machine learning and statistics, from causal discovery to evaluation of predictor fairness and out-of-distribution rob…

Opening excerpt from the authors’ abstract. source

Read the paper

OpenReview

Topics

evaluation fairness causal

← All NeurIPS 2025 Spotlight papers · Browse the whole archive