Neural Networks for Propensity Score Estimation: Simulation Results and Recommendations
Neural networks have been noted as promising for propensity score estimation because they algorithmically handle nonlinear relationships and interactions. We examine the performance neural networks as compared with main-effects logistic regression for propensity score estimation via simulation study. When the main-effects logistic propensity score model is correctly specified, the two approaches yield almost identical mean square error. When the logistic propensity score model is misspecified due to the addition of quadratic terms and interactions to the data-generating propensity score model, neural networks perform better in terms of bias and mean square error. We link the performance results to balance on observed covariates and demonstrate that our results underscore the importance of checking balance on higher-order covariate terms.
KeywordsPropensity score analysis Neural networks Logistic regression Data mining Covariate balance
This research was supported in part by the Institute of Education Sciences, U.S. Department of Education, through Grant R305D120005. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.
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