Stan and bart for causal inference: estimating heterogeneous treatment effects using the power of stan and the flexibility of machine learning

HIGHLIGHTS

• who: Vincent Dorie and collaborators from the Code for America, San Francisco, CA, USA Department of Applied Statistics, Social Science, and the Humanities, New York University have published the Article: Stan and BART for Causal Inference: Estimating Heterogeneous Treatment Effects Using the Power of Stan and the Flexibility of Machine Learning, in the Journal: Entropy 2022, 24, 1782. of 15/08/2022
• what: The authors demonstrate how stan4bart can be used to estimate average subgroup and individual-level treatment effects with stronger performance than other flexible approaches that ignore the multilevel structure of the data . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.