Personalized Treatment Selection using Causal Heterogeneity

TL;DR

This paper introduces a framework for personalized treatment selection using causal heterogeneity, improving upon traditional A/B testing by estimating individual or cohort-level treatment effects and optimizing treatment choices accordingly.

Contribution

The work develops a novel framework combining heterogeneous treatment effect estimation with constrained optimization for personalized treatment selection, validated through simulations and a real-world LinkedIn case study.

Findings

Personalized treatment selection outperforms global strategies in simulations.

The LinkedIn notification system achieved significant improvements in member visits.

The proposed methods adapt well to increasing uncertainty in treatment effect estimation.

Abstract

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.