Estimating treatment effects in the presence of unobserved confounders

Jun Wang, Wei Gao, Man Lai Tang, Changbiao Liu

Research output: Contribution to journalArticlepeer-review


Treatment effects estimation is one of the crucial mainstays in medical and epidemiological studies. Ignorance of the existence of 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 unobserved 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 the proposed estimator is stable for various unobserved confounding settings and the distribution of error terms. Finally, we apply our proposed methodologies to a low birthweight data set which yields different conclusions with and without the consideration of possible unobserved confounders.
Original languageEnglish
Pages (from-to)4685 – 4704
JournalCommunications in Statistics - Simulation and Computation
Publication statusPublished - 9 Sept 2021


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