Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya–Watson Regression

A new method for estimating the conditional average treatment effect is proposed in this paper. It is called TNW-CATE (the Trainable Nadaraya–Watson regression for CATE) and based on the assumption that the number of controls is rather large and the number of treatments is small. TNW-CATE uses the N...

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Autors principals:	Andrei Konstantinov, Stanislav Kirpichenko, Lev Utkin
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2023-04-01
Col·lecció:	Algorithms
Matèries:	treatment effect Nadaraya–Watson regression neural network shared weights meta-learner regression
Accés en línia:	https://www.mdpi.com/1999-4893/16/5/226
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