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Double machine learning for treatment and causal parameters

Chernozhukov, Victor; Chetverikov, Denis; Demirer, Mert; … - 2016

random forests, lasso, ridge, deep neural nets, boosted trees, as well as various hybrids and aggregates of these methods (e ….g. a hybrid of a random forest and lasso). We illustrate the application of the general theory through application to the …

Persistent link: https://www.econbiz.de/10011594359

Minimax semiparametric learning with approximate sparsity

Bradic, Jelena; Chernozhukov, Victor; Newey, Whitney K.; … - 2021

This paper is about the ability and means to root-n consistently and efficiently estimate linear, mean-square continuous functionals of a high dimensional, approximately sparse regression. Such objects include a wide variety of interesting parameters such as the covariance between two regression...

Persistent link: https://www.econbiz.de/10012667932

Double machine learning for treatment and causal parameters

Chernozhukov, Victor; Chetverikov, Denis; Demirer, Mert; … - 2016

Persistent link: https://www.econbiz.de/10011538313

Minimax semiparametric learning with approximate sparsity

Bradic, Jelena; Chernozhukov, Victor; Newey, Whitney K.; … - 2021

This paper is about the ability and means to root-n consistently and eﬃciently estimate linear, mean-square continuous functionals of a high dimensional, approximately sparse regression. Such objects include a wide variety of interesting parameters such as the covariance between two regression...

Persistent link: https://www.econbiz.de/10012595665

Automatic debiased machine learning of causal and structural effects

Chernozhukov, Victor; Newey, Whitney K.; Singh, Rahul - In: Econometrica : journal of the Econometric Society, an … 90 (2022) 3, pp. 967-1027

Persistent link: https://www.econbiz.de/10013279515