ObjectivesTo explore the value of neural networks (NN) in estimating propensity score, and to compare the performance of propensity score methods based on both logistic regression (LR) and NN.MethodsData sets including ten binary or continuous covariates, binary treatment variable and continuous outcome variable were simulated by SAS 9.2 software, and 5 scenarios differing by non-linear and/or non-additive associations between treatment assignment and covariates were set up. The sample sizes 500, 1000, 2000, 5000 and 10000 were considered. Propensity scores were estimated using either LR or NN model using only partial covariates associated with the outcome (methods LR1, NN1), or all covariates associated with either outcome or treatment (methods LR2, NN2). The average treatment effect (ATE) estimates, standard error (SE), bias, and mean square error (MSE) of ATE among the different models were compared.ResultsThe 95% confidence intervals of the average treatment effect were narrower in NN than that in LR models. SE, bias and MSE increased with the increasing complexity of non-linear and/or non-additive associations between the treatment and covariates, and smaller SE, bias, and MSE were observed in LR1 than LR2, and in NN1 than NN2. NN generally produced less bias than LR under most scenarios when variables associated with the outcome were introduced. SE and MSE decreased with the increasing sample size for both LR and NN models.ConclusionsNN for estimating propensity scores may be less biased and produce more precise estimates for ATE than LR in a meaningful manner when the complex association between treatment and covariates exists.