The oracle property of the generalized outcome adaptive lasso
广义结果自适应 Lasso 的 Oracle 性质
The generalized outcome-adaptive lasso (GOAL) is a variable selection for high-dimensional causal inference proposed by Baldé et al. [2023, {\em Biometrics} {\bfseries 79(1)}, 514--520]. When the dimension is high, it is now well established that an ideal variable selection method should have the oracle property to ensure the optimal large sample performance. However, the oracle property of GOAL has not been proven. In this paper, we show that the GOAL estimator enjoys the…