Double debiased machine learning nonparametric inference with continuous treatments

Kyle Colangelo and Ying-Ying Lee
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Kyle Colangelo: Institute for Fiscal Studies
Ying-Ying Lee: Institute for Fiscal Studies

No CWP72/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We propose a nonparametric inference method for causal e?ects of continuous treatment variables, under unconfoundedness and in the presence of high-dimensional or nonparametric nuisance parameters. Our simple kernel-based double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial e?ects are asymptotically normal with nonparametric convergence rates. The nuisance estimators for the conditional expectation function and the conditional density can be nonparametric kernel or series estimators or ML methods. Using doubly robust in?uence function and cross-?tting, we give tractable primitive conditions under which the nuisance estimators do not a?ect the ?rst-order large sample distribution of the DML estimators. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation to support the theoretical results and demonstrate the usefulness of our DML estimator in practice.

Date: 2019-12-17
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ore
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