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

Oregon Convention Center 

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.

randomized controlled trials

covariate adjustment

machine learning

asymptotic efficiency

model misspecification

semiparametric efficient estimators 

International Statistical Institute