Many causal and structural effects depend on regressions. Examples include average treatment effects, policy effects, average derivatives, regression decompositions, economic average equivalent variation, and parameters of economic structural models. The regressions may be high dimensional  . Plugging machine learners into identifying equations can lead to poor inference due to bias  and/or model selection. This paper gives automatic debiasing for estimating equations and valid asymptotic inference for the estimators of effects of interest . The debiasing is automatic in that its construction uses the identifying equations without the full form of the bias correction and is performed by machine learning. Novel results include convergence rates for Lasso and Dantzig learners of the bias correction,  primitive conditions for asymptotic inference for important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered. Automatic debiased machine learning (Auto-DML) is applied to estimating the average treatment effect on the treated for the NSW job training data and to estimating demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
Many causal and structural effects depend on regressions. Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models. The regressions may be high dimensional , making machine learning useful . Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection. This paper gives automatic debiasing for linear and nonlinear functions of regressions . The debiasing is automatic in using Lasso and the function of interest without the full form of the bias correction . The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high dimensional methods. In addition to providing the bias correction we give standard errors that are robust to misspecification, convergence rates for the bias correction, and primitive conditions for asymptotic inference for estimators of a variety of estimators of structural and causal effects. The automatic debiased machine learning is used to estimate the average treatment effect on the treated for the NSW job training data and to estimate demand elasticities from Nielsen scanner data while allowing preferences to be correlated with prices and income.
D
average treatment effects,
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economic average equivalent variation,
average treatment effects, causal mediation,
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, making machine learning useful
A
from regularization
R
estimating equations and valid asymptotic inference for the estimators of effects of interest
linear and nonlinear functions of regressions
R
that its construction uses the identifying equations
using Lasso and the function of interest
R
and is performed by machine learning. Novel results include
. The debiasing can be applied to any regression learner, including neural nets, random forests, Lasso, boosting, and other high dimensional methods. In addition to providing the bias correction we give standard errors that are robust to misspecification,
D
Lasso and Dantzig learners of
A
and
R
important examples, and general conditions for GMM. A variety of regression learners and identifying equations are covered. Automatic
estimators of a variety of estimators of structural and causal effects. The automatic
R
(Auto-DML) is applied to estimating
is used to estimate
R
estimating
estimate