Stan and BART for Causal Inference: Estimating Heterogeneous Treatment Effects Using the Power of Stan and the Flexibility of Machine Learning

A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where stand...

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Detalles Bibliográficos
Autores principales: Dorie, Vincent, Perrett, George, Hill, Jennifer L., Goodrich, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778579/
https://www.ncbi.nlm.nih.gov/pubmed/36554187
http://dx.doi.org/10.3390/e24121782

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778579/
https://www.ncbi.nlm.nih.gov/pubmed/36554187
http://dx.doi.org/10.3390/e24121782

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