More Powerful A/B Testing Using Auxiliary Data and Deep Learning

被引：1

作者：

Sales, Adam C. ^{[1
]}

Prihar, Ethan ^{[1
]}

Gagnon-Bartsch, Johann ^{[2
]}

Gurung, Ashish ^{[1
]}

Heffernan, Neil T. ^{[1
]}

机构：

[1] Worcester Polytech Inst, Worcester, MA 01609 USA

[2] Univ Michigan, Ann Arbor, MI 48109 USA

来源：

ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II | 2022年 / 13356卷

关键词：

D O I：

10.1007/978-3-031-11647-6_107

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Randomized A/B tests allow causal estimation without confounding but are often under-powered. This paper uses a new dataset, including over 250 randomized comparisons conducted in an online learning platform, to illustrate a method combining data from A/B tests with log data from users who were not in the experiment. Inference remains exact and unbiased without additional assumptions, regardless of the deep-learning model's quality. In this dataset, incorporating auxiliary data improves precision consistently and, in some cases, substantially.

引用

页码：524 / 527

页数：4

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