Automatic Debiased Machine Learning of Causal and Structural Effects
Name
1809.05224.pdf
Description
Submitted version
Size
519.71 KB
Format
Adobe PDF
Checksum (MD5)
569e04a02197c9df0e667bdbcd14a4aa
Author(s)
Chernozhukov, Victor
Newey, Whitney K
Singh, Rahul
Date Issued
May 2022
Journal
Econometrica
Publisher
The Econometric Society
Citation
Chernozhukov, Victor, Newey, Whitney K and Singh, Rahul. 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects." Econometrica, 90 (3).
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Statistics and Data Science Center (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.3982/ecta18515
