报告题目：Estimation of treatment effects in nonlinear models and its applications.
报告时间：2019年12月30日下午3:00 - 5:00.
报告摘要：Estimating the effect of medical treatments on subject responses is one of the crucial problems in medical research. Matched-pairs designs are commonly implemented in the field of medical research to eliminate confounding and improve efficiency. In this article, new estimators of treatment effects for heterogeneous matched-pairs data are proposed. Asymptotic properties of the proposed estimators are derived. Simulation studies show that the proposed estimators have some advantages over the famous Heckman’s estimator, the conditional maximum likelihood estimator, and the inverse probability weighted estimator. Ignorance of the existence of the unobserved confounders may result in biased estimators. 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 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. We apply the proposed methodology to a data set from a study of low-birth-weight infants.