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3 - Personalizing Medicine

Estimating Heterogeneous Treatment Effects

from Part I - Personalized Medicine

Published online by Cambridge University Press:  21 April 2022

By
Tony Duan  and
Sanjay Basu
Edited by
Sze-chuan Suen ,
David Scheinker  and
Eva Enns
Sze-chuan Suen
Affiliation:
University of Southern California
David Scheinker
Affiliation:
Stanford University, California
Eva Enns
Affiliation:
University of Minnesota
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Summary

We describe fundamental challenges to estimating heterogeneous treatment effects in the context of the statistical causal inference literature, proposed algorithms for addressing those challenges, and methods to evaluate how well heterogeneous treatment effects have been estimated. We illustrate the proposed algorithms using data from two large randomized trials of blood pressure treatments. We describe directions for future research in medical statistics and machine learning in this domain. The focus will be on how flexible machine learning methods can improve causal estimators, especially in the RCT setting.

Keywords

Statisticscausal inferencealgorithmstreatment effectsblood pressure treatmentmachine learning
Type
Chapter
Information
Artificial Intelligence for Healthcare
Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
 , pp. 37 - 59
Publisher: Cambridge University Press
Print publication year: 2022

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