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Abstract:
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The Propensity score is one of the approaches that have been widely used in many fields to remove the effect of confounding by comparing outcomes in treatment and control groups. Even though many observational studies have focused on the studying binary treatments over last decades, examining multiple treatment conditions has studied very limited attention in literature. So, I used the Monte Carlo simulation to examine performance of propensity score methods to make estimation the effect of multi-treatment groups on continuous outcomes. These methods have been performed in average treatment effect under the using different level of hidden bias. Then, I would like to continue comparing methods across the complexity of relationship between exposure/outcomes and covariates in propensity score methods under various conditions. Thus, the using Monte Carlo simulation evaluates how the choice of variables involving propensity score models effect both of treated and untreated subjects who are matched.
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