Omitted Variable Bias of Lasso-Based Inference Methods: A Finite Sample Analysis
研究了基于Lasso的推断方法（如post–double Lasso和debiased Lasso）在有限样本中的行为，发现即使系数稀疏且样本量大于控制变量数，这些方法仍可能因Lasso未选择相关控制变量而产生显著遗漏变量偏差，对实证应用中的渐近理论使用提出警示。
Abstract We study the finite sample behavior of Lasso-based inference methods such as post–double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso's not selecting relevant controls. This phenomenon can occur even when the coefficients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern high-dimensional OLS-based methods and provide practical guidance.