【Metrics Seminar】Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments
研討會日期 : 2022-12-27
時間 : 14:30
主講人 : Professor Ying-Ying Lee
地點 : Conference room B110
主持人 : Professor Chu-An Liu
演講者簡介 : Professor Ying-Ying Lee received her Ph.D. in Economics from the University of Wisconsin-Madison in 2013. She is currently an Associate professor at the University of California, Irvine. Her research interests are Econometric Theory, and Empirical Microeconomics.
演講摘要 : We propose a nonparametric inference method for causal effects of continuous treatment variables, under unconfoundedness and nonparametric or high-dimensional nuisance parameters. Our double debiased machine learning (DML) estimators for the average dose-response function (or the average structural function) and the partial effects are asymptotically normal with nonparametric convergence rates. The nuisance estimators for the conditional expectation function and the conditional density can be nonparametric or ML methods. Utilizing a kernel-based doubly robust moment function and cross-fitting, we give high-level conditions under which the nuisance estimators do not affect the first-order large sample distribution of the DML estimators. We further provide sufficient low-level conditions for kernel, series, and deep neural networks. We propose a data-driven bandwidth to consistently estimate the optimal bandwidth that minimizes the asymptotic mean squared error. We justify the use of kernel to localize the continuous treatment at a given value by the Gateaux derivative. We implement various ML methods in Monte Carlo simulations and an empirical application on a job training program evaluation.