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. epidemiologic methods; inverse probability of treatment weighting; machine learning; matching; propensity score; Super Learner Abbreviations: ASAM, average standardized absolute mean difference; ATE, average treatment effect; AUROC, area under the receiver operating characteristic curve; CART, classification and regression trees; IPTW, inverse probability of treatment weighting; PS, propensity score; SL, Super Learner. Methods based on the propensity score (PS) (1) have become a common approach to causal effect estimation, especially in the medical literature (2, 3). These methods rely on estimation of an individual's probability of receiving a treatment conditional on a set of observed covariates (1). The estimated PS may be used to match treated persons with untreated persons (4), as a covariate in a regression of the outcome on the PS and exposure (1, 5), or to reweight the sample in order to estimate the treatment effect (6). While investigators have extensively studied the best way to use PS to better balance the distributions of covariates between the treated and the untreated (7) and to provide an optimal bias/variance tradeoff for the estimation of treatment effects (8-10), there are few guidelines on how to estimate the PS. A common practice is to use logistic regression (11). Rubin (12) recommended using complex, nonparsimonious PS models, including interactions and/or quadratic terms (6). However, estimating the PS using a parametric model requires accepting strong assumptions concerning the functional form of the relationship between treatment allocation and the covariates. PS model misspecification may in turn both affect the covariate balance and result in bias in the treatment effect estimate (13-15). Hence, some have argued for and applied data-adaptive methods (6, (16) (17) (18) (19) (20) (21) . Classification trees or neural networks have been proposed as an alternative to parametric models for PS estimation (22, 23) . However, the question of the best method remains unanswered. Moreover, if, for a particular cohort, a parametric logistic regression
REF shows experimentally that the super learner method could achieve a significantly better performance under the situation of severe model misspecification.
Improving Propensity Score Estimators' Robustness to Model Misspecification Using Super Learner
American journal of epidemiology
American journal of epidemiology
Medicine