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• W2979658147 abstract "When interpreting A/B tests, we typically focus only on the statistically significant results and take them by face value. This practice, termed post-selection inference in the statistical literature, may negatively affect both point estimation and uncertainty quantification, and therefore hinder trustworthy decision making in A/B testing. To address this issue, in this paper we explore two seemingly unrelated paths, one based on supervised machine learning and the other on empirical Bayes, and propose post-selection inferential approaches that combine the strengths of both. Through large-scale simulated and empirical examples, we demonstrate that our proposed methodologies stand out among other existing ones in both reducing post-selection biases and improving confidence interval coverage rates, and discuss how they can be conveniently adjusted to real-life scenarios." @default.
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• W2979658147 date "2021-08-14" @default.
• W2979658147 modified "2023-09-23" @default.
• W2979658147 title "On Post-selection Inference in A/B Testing" @default.
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• W2979658147 doi "https://doi.org/10.1145/3447548.3467129" @default.
• W2979658147 hasPublicationYear "2021" @default.
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