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Covariate adjustment in randomized trials: comparison of machine learning and parametric models

Abstract Number:

2299 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Ryo Hanaoka (1), Kentaro Sakamaki (2), Tomohiro Shinozaki (1)

Institutions:

(1) Tokyo University of Science, Japan, (2) Juntendo University, Japan

Co-Author(s):

Kentaro Sakamaki  
Juntendo University
Tomohiro Shinozaki  
Tokyo University of Science

First Author:

Ryo Hanaoka  
Tokyo University of Science

Presenting Author:

Ryo Hanaoka  
N/A

Abstract Text:

For estimating the average treatment effect in randomized trials, covariate adjustment improves the efficiency of an estimator with minimal impact on bias and type 1 error. However, there have been insufficient comparisons between parametric models and machine learning-based causal inference methods in randomized settings, specifically considering the trade-offs between a specified model's correctness and its parametric constraints. This study aims to compare the efficiency among the following methods: 1) linear regression models, 2) meta-learners (machine learning-based S-, T-, X-, and DR-learners), and 3) augmented inverse probability weighted estimators (semiparametric or nonparametric machine learning-based specification). In simulation study, the efficiency is improved by meta-learners to the same extent as or more than parametric model, regardless of the correctness of the specification of parametric model. However, some methods have issues such as bias to the null for S-learner. Considering both efficiency and bias, we conclude that DR-learner is a viable potion in modest-sized trials.

Keywords:

randomized controlled trials|covariate adjustment|machine learning|asymptotic efficiency|model misspecification|semiparametric efficient estimators

Sponsors:

International Statistical Institute

Tracks:

Miscellaneous

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