Stability of Inference Derived from Machine Learning-based Doubly Robust Estimators of Treatment Effects Open Access

Abstract

Doubly robust targeted minimum loss-based estimator (DRTMLE) is a causal inference technique used to estimate the covariate-adjusted treatment effects. These estimators often involve the use of super learning, a flexible regression technique that involves cross-validation. Accordingly, estimates and inference obtained using this methodology may change when different seeds are set to control the random splitting process. This may decrease the trustworthiness of such analyses. In this paper, we evaluate two solutions to this problem. Simulation studies are presented that assess the performance of both tactics in different scenarios, and a real data analysis is presented. We conclude that by averaging estimates over repeated runs with different seeds set, more stable performance is achieved without deleterious effect on estimator performance.

Table of Contents

1.     Introduction 1

2.     Methods 3

2.1 Causal Inference with Doubly Robust Methods 3

2.2 Super Learner   5

2.3 Dependence of Results on Random Number Generation 7

2.4 Proposed Solutions 9

3.     Simulation 9

3.1 Study Design 9

3.2 Results 11

4.     Implementation on Clinical Study of Tuberculosis Drug-Resistance 15

5.     Discussion 18

 

References 19

Appendix: Tables and Figures 21

About this Master's Thesis

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School	Rollins School of Public Health
Department	Biostatistics
Subfield / Discipline	Biostatistics - MPH & MSPH
Degree	M.S.P.H.
Submission	Master's Thesis
Language	English
Research Field	Biology, Biostatistics Statistics
Keyword	Machine Learning Super Learning DRTMLE Causal Inference
Committee Chair / Thesis Advisor	David Benkeser, Emory University
Committee Members	Zhaohui Qin, Emory University

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