MINIMAX OPTIMAL CONDITIONAL INDEPENDENCE TESTING

• 成果类型: Article
• 署名作者: Neykov, Matey; Balakrishnan, Sivaraman; Wasserman, Larry
• 署名单位: Carnegie Mellon University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/20-AOS2030
• 发表日期: 2021
• 页码: 2151-2177
• 关键词: high-dimensional multinomials

摘要

We consider the problem of conditional independence testing of X and Y given Z where X, Y and Z are three real random variables and Z is continuous. We focus on two main cases-when X and Y are both discrete, and when X and Y are both continuous. In view of recent results on conditional independence testing [Ann. Statist. 48 (2020) 1514-1538], one cannot hope to design nontrivial tests, which control the type I error for all absolutely continuous conditionally independent distributions, while still ensuring power against interesting alternatives. Consequently, we identify various, natural smoothness assumptions on the conditional distributions of X, Y vertical bar Z = z as z varies in the support of Z, and study the hardness of conditional independence testing under these smoothness assumptions. We derivematching lower and upper bounds on the critical radius of separation between the null and alternative hypotheses in the total variation metric. The tests we consider are easily implementable and rely on binning the support of the continuous variable Z. To complement these results, we provide a new proof of the hardness result of Shah and Peters.