False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments

TL;DR

This paper introduces statistical methods to detect and analyze heterogeneity in treatment effects across different user groups in online experiments, ensuring controlled false discovery rates and providing actionable insights.

Contribution

It presents novel methods for identifying heterogeneous treatment effects in A/B testing with controlled FDR, enabling more nuanced understanding of user-specific impacts.

Findings

Methods work robustly on simulation and real data

Toolkit deployed at Snap for large-scale experiments

Provides insights into user group heterogeneity

Abstract

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.