EcoSta 2022: Start Registration
View Submission - EcoSta2022
A0650
Title: Causal trees and forest with sufficient dimension reduction Authors:  Tomoshige Nakamura - Keio University (Japan) [presenting]
Hiroshi Shiraishi - Keio University (Japan)
Abstract: The causal trees and forests are one of the methods for nonparametric statistical estimation method for individual causal effects based on random forests. We consider causal trees and causal forests that use sufficient dimension reduction (SDR) techniques to approximate a locally adaptive kernel.