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Activity Number: 477 - Statistical Methods for New Age Marketing Problems
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Marketing
Abstract #323163
Title: Personalized Treatment Selection Using Causal Heterogeneity
Author(s): Kinjal Basu*
Companies: LinkedIn Corporation
Keywords: A/B Testing; Experiments; Heterogeneity; Optimal Allocation
Abstract:

Randomized experimentation 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 also demonstrate the effectiveness of the method through a real-life example related to serving notifications at Linkedin. Any marketing experiment can leverage this framework for maximizing the benefits across a global population.


Authors who are presenting talks have a * after their name.

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