Sequential Conditional Independence Testing with Machine Learning Models

Key points

• Log-optimal e-variables have been studied in this setting, but it remains unclear how to incorporate machine learning models into their design.1
• Other approaches test exchangeability directly, yielding an e-variable with lower power in theory but, surprisingly, higher power in practice.1
• The decomposition shows that GRO e-variable estimates can be beaten because of their worse approximation and estimation errors, and we explore intermediate null hypotheses between model-X conditional independence and exchangeability to reduce these errors.1
• We provide estimation error bounds that accommodate triple robustness results, achieving fast convergence rates.1

Sources (1)

• [1]Sequential Conditional Independence Testing with Machine Learning Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 07:18 AM

  Conditional independence testing is a ubiquitous problem in scientific discovery.

  Log-optimal e-variables have been studied in this setting, but it remains unclear how to incorporate machine learning models into their design.

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