Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models
This work addresses estimation challenges in econometrics and causal inference for researchers, though it is incremental as it builds on existing orthogonal machine learning methods.
The paper tackles the problem of estimating high-dimensional sparse parameters in nonlinear semiparametric models with nuisance functions, proposing a Lasso-type estimator that converges at the oracle rate, as demonstrated in an application to Connecticut's Jobs First welfare reform experiment.
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 regularization 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.