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cs.LG2025
Practical Kernel Tests of Conditional Independence
Roman Pogodin, Antonin Schrab, Yazhe Li +2
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the corre…
cs.LG2024
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side informa…