Affiliation:
1. Institute of Psychology University of Münster Münster Germany
Abstract
There has been growing interest in using nonparametric machine learning approaches for propensity score estimation in order to foster robustness against misspecification of the propensity score model. However, the vast majority of studies focused on single‐level data settings, and research on nonparametric propensity score estimation in clustered data settings is scarce. In this article, we extend existing research by describing a general algorithm for incorporating random effects into a machine learning model, which we implemented for generalized boosted modeling (GBM). In a simulation study, we investigated the performance of logistic regression, GBM, and Bayesian additive regression trees for inverse probability of treatment weighting (IPW) when the data are clustered, the treatment exposure mechanism is nonlinear, and unmeasured cluster‐level confounding is present. For each approach, we compared fixed and random effects propensity score models to single‐level models and evaluated their use in both marginal and clustered IPW. We additionally investigated the performance of the standard Super Learner and the balance Super Learner. The results showed that when there was no unmeasured confounding, logistic regression resulted in moderate bias in both marginal and clustered IPW, whereas the nonparametric approaches were unbiased. In presence of cluster‐level confounding, fixed and random effects models greatly reduced bias compared to single‐level models in marginal IPW, with fixed effects GBM and fixed effects logistic regression performing best. Finally, clustered IPW was overall preferable to marginal IPW and the balance Super Learner outperformed the standard Super Learner, though neither worked as well as their best candidate model.
Subject
Statistics and Probability,Epidemiology
Reference53 articles.
1. Evaluating uses of data mining techniques in propensity score estimation: a simulation study
2. Improving propensity score weighting using machine learning
3. Improving Propensity Score Estimators' Robustness to Model Misspecification Using Super Learner
4. NicholsA McBrideL.Propensity scores and causal inference using machine learning methods. Paper presented at: Presentation in the Track Session “Machine Learning in Applied Economics” at the Annual Meeting of the Agicultural and Applied Economics Association (AAEA); July 2019; Atlanta GA:21‐23.