STICERD Econometrics Seminar Series
Automatic debiased machine learning of causal and structural effects
Whitney Newey (MIT)
Thursday 18 February 2021 14:00 - 15:30
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About this event
Many causal and structural effects depend on regressions. Examples include policy effects, average treatment effects, causal mediation, and structural parameters of economic 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 a variety of estimators of structural and causal effects. The automatic debiased machine learning 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
STICERD Econometrics seminars are held on Thursdays in term time at 14.00-15.30, ONLINE, unless specified otherwise.
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