Endogenous Treatment Effect Estimation with a Large and Mixed Set of Instruments and Control Variables
提出一种数据驱动方法，将大量混合协变量自动分类为工具变量、控制变量或噪声，使估计量具备先知性质，适用于内生处理效应研究。
Abstract Instrumental variables (IVs) and control variables are frequently used to assist researchers in investigating endogenous treatment effects. When used together, their identities are typically assumed to be known. However, in many practical situations, one is faced with a large and mixed set of covariates, some of which can serve as excluded IVs, some can serve as control variables, whereas others should be discarded from the model. It is often not possible to classify them based on economic theory alone. This paper proposes a data-driven method to classify a large (increasing with sample size) set of covariates into excluded IVs, controls, and noise to be discarded. The resulting IV estimator is shown to have the oracle property (to have the same first-order asymptotic distribution as the IV estimator, assuming the true classification is known).