Treatment effects estimation is one of the crucial mainstays in medical and epidemiological studies. Ignorance of the existence of confounders may result in biased estimates. The issue will become more serious and complicated if the treatment is endogenous (i.e., the presence of unobserved confounders). In this article, we propose a new treatment effects estimator for binary treatments in observational studies in the presence of unobservable confounders. The proposed estimator is shown to be consistent and asymptotically normally distributed. A statistic is also developed for testing the existence of treatment effects. Simulation studies show that our proposed estimator is relatively stable for various unobservable confounding settings. Finally, we apply our proposed methodologies to a low birthweight data set which yields different conclusions with and without the consideration of possible unobservable confounders.
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