@inproceedings{deng2021on,
author = {Deng, Alex and Li, Yicheng and Lu, Jiannan and Ramamurthy, Vivek},
title = {On Post-selection Inference in A/B Testing},
organization = {ACM},
booktitle = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
year = {2021},
month = {August},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/on-post-selection-inference-in-a-b-testing/},
}