En savoir plus

Lundi 23 mai 2022

Edvard Bakhitov (UPenn) présentera Automatic Debiased Machine Learning in Presence of Endogeneity.

Résumé :

Recent advances in machine learning literature provide a series of new algorithms that both address endogeneity and can be applied in high-dimensional environments, we call them MLIV. This paper introduces an approach for performing valid asymptotic inference on regular functionals of MLIV estimators. The approach is based on construction of an orthogonal moment function that has a zero derivative with respect to the MLIV estimator. The debiasing is automatic in the sense that it only depends on the form of the identifying moment function but not on the form of the bias correction term. We derive a convergence rate for the penalized GMM estimator of the bias correction term. We also give conditions for root-n consistency and asymptotic normality of the debiased MLIV estimator of the functional of interest. Overall, the approach allows for a large variety of MLIV estimators as long as they satisfy mild convergence rate conditions. We apply our procedure to estimate the conditional demand derivative within the nonparametric demand for differentiated goods framework. Using both simulated and real data, we demonstrate that our debiased estimates have significantly reduced bias and close to the nominal level coverage, while the plug-in estimates perform poorly.