We present tests for conditional independence of discrete variables that can be applied in a sequential setting with Type-I error probability guarantee. Power of these tests can be improved by incorporating hypothesized effect size or by sharing information between strata. Both scenarios are illustrated through simulations.

Philips Research
OCUPAI'22 - Online Conference to Unite Philips AI 2022
Machine Learning

Turner, R.J, Grünwald, P.D, & Härmä, A. (2022). Safe Sequential Conditional Independence Tests for Discrete Variables. In Proceedings of the Online Conference to Unite Philips AI 2022.