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Double/debiased machine learning for logistic partially linear model The state of applied econometrics: Causality and policy evaluation

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https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1093%2Fectj%2Futab019;h=repec:oup:emjrnl:v:24:y:2021:i:3:p:559-588.
Time Added
6/20/2022, 12:18:29 PM
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Machine Learning
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Authors
Molei Liu Yi Zhang and Doudou Zhou
Abstract
SummaryWe propose double/debiased machine learning approaches to infer a parametric component of a logistic partially linear model. Our framework is based on a Neyman orthogonal score equation consisting of two nuisance models for the nonparametric component of the logistic model and conditional mean of the exposure with the control group. To estimate the nuisance models we separately consider the use of high dimensional (HD) sparse regression and (nonparametric) machine learning (ML) methods. In the HD case we derive certain moment equations to calibrate the first order bias of the nuisance models which preserves the model double robustness property. In the ML case we handle the nonlinearity of the logit link through a novel and easy-to-implement ‘full model refitting’ procedure. We evaluate our methods through simulation and apply them in assessing the effect of the emergency contraceptive pill on early gestation and new births based on a 2008 policy reform in Chile.
Keywords
Logistic partially linear model ; double machine learning ; double robustness ; regularized regression ; calibration (search for similar items in EconPapers)
Year Published
2021
Series
Econometrics Journal 2021 vol. 24 issue 3 559-588
Rank
0.68
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