Testing Conditional Independence in Supervised Learning Algorithms

28 Jan 2019 David S. Watson Marvin N. Wright

We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set. Building on the knockoff framework of Cand\`es et al. (2018), we develop a novel testing procedure that works in conjunction with any valid knockoff sampler, supervised learning algorithm, and loss function... (read more)

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