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Personalized Treatment Selection using Causal Heterogeneity

Authors:

Cyrus DiCiccio,

Padmini Jaikumar,

Shaunak ChatterjeeAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 1574 - 1585

https://doi.org/10.1145/3442381.3450075

Published: 03 June 2021 Publication History

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.

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Cited By

Sun ZYang HLiu DWeng YTang XHe X(2024)End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift ModelingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688147(560-569)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688147
Quin FWeyns DGalster MSilva C(2024)A/B testingJournal of Systems and Software10.1016/j.jss.2024.112011211:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112011
Cui DGuo JLiu PZhang X(2024)Using Case-Based Causal Reasoning to Provide Explainable Counterfactual Diagnosis in Personalized Sprint TrainingCase-Based Reasoning Research and Development10.1007/978-3-031-63646-2_27(418-429)Online publication date: 24-Jun-2024
https://doi.org/10.1007/978-3-031-63646-2_27
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Published In

cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
Wikimedia Foundation

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Published: 03 June 2021

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WWW '21: The Web Conference 2021

April 19 - 23, 2021

Ljubljana, Slovenia

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Cited By

Sun ZYang HLiu DWeng YTang XHe X(2024)End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift ModelingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688147(560-569)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688147
Quin FWeyns DGalster MSilva C(2024)A/B testingJournal of Systems and Software10.1016/j.jss.2024.112011211:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112011
Cui DGuo JLiu PZhang X(2024)Using Case-Based Causal Reasoning to Provide Explainable Counterfactual Diagnosis in Personalized Sprint TrainingCase-Based Reasoning Research and Development10.1007/978-3-031-63646-2_27(418-429)Online publication date: 24-Jun-2024
https://doi.org/10.1007/978-3-031-63646-2_27
Nandy PYu XLiu WTu YBasu KChatterjee S(2023)Generalized Causal Tree for Uplift Modeling2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386842(788-798)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386842

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