Sequential Conditional Independence Testing with Machine Learning Models

arXiv:2610.11388 · math.ST, stat.ML, stat.TH · Submitted 2026-10-08 · Read on arXiv

math.ST, stat.ML, stat.TH

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/Antares01/sequential_ml_testing

Terminology

• Exchangeability — 57× in this paper · explained in 2 episodes
• Machine Learning — 35× in this paper
• Robustness — 17× in this paper · explained in 5 episodes
• Wasserstein Distance — 7× in this paper · explained in 4 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Conditional Randomization Test — 2× in this paper
• Distillation — 2× in this paper · explained in 2 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Admissibility — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• When Knockoffs fail: diagnosing and fixing non-exchangeability of Knockoffs
• Rao-Blackwellized e-variables
• Asymptotic and compound e-values: multiple testing and empirical Bayes
• Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression
• Measuring Evidence against Exchangeability and Group Invariance with E-values
• Testing hypotheses generated by constraints
• TabICLv2: A better, faster, scalable, and open tabular foundation model
• Conditional Feature Importance revisited: Double Robustness, Efficiency and Inference

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