Bootstrap Confidence Intervals for the Estimation of Average Treatment Effect on Propensity Score

Xia Peng, Zi'ou Feng

Abstract


Causal inferences on the average treatment effect in observational studies are always difficult problems because the distributions of samples in the two treatment groups can not be observed at the same time, and the estimation of the treatment effect is often biased.In this paper, the propensity score and the propensity score subclassification, selected from several methods, are used to assess the treatment effect.The estimation of the average treatment effect give the Bootstrap confidence intervals. Simulation studies are inducted for the continuous samples in normal distribution and the mixed samples of discrete and continuous type.

Full Text: PDF DOI: 10.5539/jmr.v3n3p52

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This work is licensed under a Creative Commons Attribution 3.0 License.

Journal of Mathematics Research   ISSN 1916-9795 (Print)   ISSN 1916-9809 (Online)

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