Robust estimation of heterogeneous treatment effects: an algorithm-based approach

If you need an accessible version of this item, please email your request to digschol@iu.edu so that they may create one and provide it to you.
Date
2021
Language
American English
Embargo Lift Date
Committee Members
Degree
Degree Year
Department
Grantor
Journal Title
Journal ISSN
Volume Title
Found At
Taylor & Francis
Abstract

Heterogeneous treatment effect estimation is an essential element in the practice of tailoring treatment to suit the characteristics of individual patients. Most existing methods are not sufficiently robust against data irregularities. To enhance the robustness of the existing methods, we recently put forward a general estimating equation that unifies many existing learners. But the performance of model-based learners depends heavily on the correctness of the underlying treatment effect model. This paper addresses this vulnerability by converting the treatment effect estimation to a weighted supervised learning problem. We combine the general estimating equation with supervised learning algorithms, such as the gradient boosting machine, random forest, and artificial neural network, with appropriate modifications. This extension retains the estimators’ robustness while enhancing their flexibility and scalability. Simulation shows that the algorithm-based estimation methods outperform their model-based counterparts in the presence of nonlinearity and non-additivity. We developed an R package, RCATE, for public access to the proposed methods. To illustrate the methods, we present a real data example to compare the blood pressure-lowering effects of two classes of antihypertensive agents.

Description
item.page.description.tableofcontents
item.page.relation.haspart
Cite As
Li, R., Wang, H., Zhao, Y., Su, J., & Tu, W. (2021). Robust estimation of heterogeneous treatment effects: An algorithm-based approach. Communications in Statistics - Simulation and Computation, 0(0), 1–18. https://doi.org/10.1080/03610918.2021.1974883
ISSN
0361-0918, 1532-4141
Publisher
Series/Report
Sponsorship
Major
Extent
Identifier
Relation
Journal
Communications in Statistics - Simulation and Computation
Source
Author
Alternative Title
Type
Article
Number
Volume
Conference Dates
Conference Host
Conference Location
Conference Name
Conference Panel
Conference Secretariat Location
Version
Author's manuscript
Full Text Available at
This item is under embargo {{howLong}}