Personalized Treatment Selection using Causal Heterogeneity
This work addresses the need for more effective treatment selection in internet industry A/B testing by moving beyond global strategies to personalization, though it is incremental as it builds on existing causal heterogeneity methods.
The authors tackled the problem of selecting optimal treatments in A/B testing by developing a personalized framework that estimates heterogeneous treatment effects and optimizes selection for cohorts or individuals, resulting in significant improvements over global selection and heuristic methods, such as increased member visits in a LinkedIn notification case.
Randomized experimentation (also known as A/B testing or bucket testing) is widely used in the internet industry to measure the metric impact obtained by different treatment variants. A/B tests identify the treatment variant showing the best performance, which then becomes the chosen or selected treatment for the entire population. However, the effect of a given treatment can differ across experimental units and a personalized approach for treatment selection can greatly improve upon the usual global selection strategy. In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization. We perform a two-fold evaluation of our proposed methods. First, a simulation analysis is conducted to study the effect of personalized treatment selection under carefully controlled settings. This simulation illustrates the differences between the proposed methods and the suitability of each with increasing uncertainty. We also demonstrate the effectiveness of the method through a real-life example related to serving notifications at Linkedin. The solution significantly outperformed both heuristic solutions and the global treatment selection baseline leading to a sizable win on top-line metrics like member visits.