Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression

Séminaire d’économétrie de Montréal 2022-2023
conjoint avec les départements d’économique des universités de Montréal, du Québec à Montréal, Concordia et McGill ainsi que le CIRANO

salle ARTS 160 (McGill University, 853, rue Sherbrooke Ouest)

Responsables : Marine Carrasco (U. de Montréal) et Saraswata Chaudhuri (McGill U.)

 
RÉSUMÉ: We give debiased machine learners of parameters of interest that depend on generalized linear regressions, which regressions make a residual orthogonal to regressors. The parameters of interest include many causal and policy effects. We give neural net learners of the bias correction that are automatic in only depending on the object of interest and the regression residual. Convergence rates are given for these neural nets and for more general learners of the bias correction. We also give conditions for asymptotic normality and consistent asymptotic variance estimation of the learner of the object of interest. We find that the resulting estimator of the average treatment effect outperforms a state of the art neural net estimator based on inverse propensity score weighting in a simulation study.