Abstract
This study compared the average treatment effect on the treated(ATT) of three propensity score methods- logistic regression model, generalized boosted model, and Bayesian model– by simulation. The simulated data were generated under two sample sizes, four covariates models, and four model intercepts conditions. The results shaw that generalized boosted model and Bayesian model also provide smaller bias than logistic regression model when the sample size was small(N=200). And, generalized boosted model and Bayesian model provide small bias than logistic regression model. It was interpreted that the propensity score method which takes into account the distribution of covariates produce more adequate estimation of causal effect.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
