Regularised orthogonal machine learning for nonlinear semiparametric models
提出一种Lasso型估计量，用于高维稀疏参数估计，该参数由单指标条件矩约束识别，并允许矩函数依赖机器学习估计的干扰函数，通过正交化消除正则化偏差，达到Oracle收敛速度。
Summary This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools. We first adjust the moment function so that the gradient of the future loss function is insensitive (formally, Neyman orthogonal) with respect to the first-stage regularisation bias, preserving the single index property. We then take the loss function to be an indefinite integral of the adjusted moment function with respect to the single index. The proposed Lasso estimator converges at the oracle rate, where the oracle knows the nuisance function and solves only the parametric problem. We demonstrate our method by estimating the short-term heterogeneous impact of Connecticut’s Jobs First welfare reform experiment on women’s welfare participation decision.