Abstract
Endogenous regressors can lead to biased estimates for causal effects using methods assuming regressor-error independence. To correct for endogeneity bias, the authors propose a new method that accounts for the regressor-error dependence using flexible semiparametric odds ratio conditional models; the approach requires neither parametric distributional assumptions nor tuning parameters for modeling endogenous regressors' distributions conditional on the error term and exogenous regressors. Inference is achieved via optimizing the profile likelihood concentrating on the parameters of interest. The proposed approach requires no use of instrumental variables (IVs), observed or latent, that must satisfy the stringent condition of exclusion restriction. Nonnormally distributed endogenous regressors are required for model identification with a normal error distribution. The approach's flexibility in capturing regressor-error dependence increases the capability of IV-free endogeneity correction and provides opportunities to improve the accuracy of causal effect estimation. Unlike existing IV-free methods, the proposed approach can handle discrete endogenous regressors with few levels, such as binary regressors or count regressors with small means, and is thus applicable to a plethora of applications involving such regressors. The authors demonstrate the versatility of the approach for binary, count, and continuous endogenous regressors using comprehensive simulation studies and empirical data.