Automatic Debiased Machine Learning of Causal and Structural Effects.
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 usefu...
| Published in: | Econometrica Vol. 90; no. 3; pp. 967 - 1028 |
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| Main Authors: | , , |
| Format: | Article |
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Wiley-Blackwell
May2022
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=157125017&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157125017 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: May2022 vid: 90 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 157125017 10.3982/ECTA18515 ppf: 967 ppct: 61 formats: tig: atl: Automatic Debiased Machine Learning of Causal and Structural Effects. aug: au: Chernozhukov, Victor Newey, Whitney K. Singh, Rahul affil: Department of Economics, Massachusetts Institute of Technology NBER su: New South Wales Elasticity (Economics) Machine learning Random forest algorithms Nonlinear regression Unemployment statistics Nonlinear functions sug: subj: Elasticity (Economics) New South Wales Machine learning Random forest algorithms Nonlinear regression Unemployment statistics Nonlinear functions keyword: causal parameters Debiased machine learning Lasso regression effects Riesz representation structural parameters causal parameters Debiased machine learning Lasso regression effects Riesz representation structural parameters ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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