title: Improving propensity score estimators' robustness to model misspecification using super learner.
abstract: The consistency of propensity score (PS) estimators relies on correct specification of the PS model. The PS is frequently estimated using main-effects logistic regression. However, the underlying model assumptions may not hold. Machine learning methods provide an alternative nonparametric approach to PS estimation. In this simulation study, we evaluated the benefit of using Super Learner (SL) for PS estimation. We created 1,000 simulated data sets (n = 500) under 4 different scenarios characterized by various degrees of deviance from the usual main-term logistic regression model for the true PS. We estimated the average treatment effect using PS matching and inverse probability of treatment weighting. The estimators' performance was evaluated in terms of PS prediction accuracy, covariate balance achieved, bias, standard error, coverage, and mean squared error. All methods exhibited adequate overall balancing properties, but in the case of model misspecification, SL performed better for highly unbalanced variables. The SL-based estimators were associated with the smallest bias in cases of severe model misspecification. Our results suggest that use of SL to estimate the PS can improve covariate balance and reduce bias in a meaningful manner in cases of serious model misspecification for treatment assignment.
authors.forename: Romain
authors.initials: R
authors.lastname: Pirracchio
authors.forename: Maya L
authors.initials: ML
authors.lastname: Petersen
authors.forename: Mark
authors.initials: M
authors.lastname: van der Laan
mesh_terms: D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans; D057216:Propensity Score
keywords: Super Learner; epidemiologic methods; inverse probability of treatment weighting; machine learning; matching; propensity score