2018/07/19 by Yuxiang Xie, Nanyu Chen, Xiaolin Shi
Business, Management and Accounting · Mathematics · #Advanced Causal Inference Techniques #Average treatment effect #Consumer Market Behavior and Pricing #False discovery rate #Feature (linguistics) #Measure (data warehouse) #Product (mathematics) #Statistical Methods in Clinical Trials #Statistical hypothesis testing #The Internet #Treatment effect #msc:62 #stat.AP
paper · pdf · doi:10.1145/3219819.3219860
published as Yuxiang Xie, Nanyu Chen, and Xiaolin Shi. 2018. KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining Pages 876-885
openalex created_date 2018/06/29 · openalex publication_date 2018/07/19 · arxiv created 2018/08/14 · arxiv updated 2018/08/16 · openalex updated_date 2026/08/05
Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose statistical methods that can systematically and accurately identify Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g. mobile device type, country), and determine which factors (e.g. age, gender) of users contribute to the heterogeneity of the treatment effect in an A/B test. By applying these methods on both simulation data and real-world experimentation data, we show how they work robustly with controlled low False Discover Rate (FDR), and at the same time, provides us with useful insights about the heterogeneity of identified user groups. We have deployed a toolkit based on these methods, and have used it to measure the Heterogeneous Treatment Effect of many A/B tests at Snap.