Estimating Individual Treatment Effect in Observational Data Using
Random Forest Methods

Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

19 January 2017

Gian-Andrea Thanei

Daniel J. Feaster

Papers citing "Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods"

9 / 9 papers shown

Title
Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks Sehwan Kim F. Liang FedML 41 0 0 04 May 2025
Linking a predictive model to causal effect estimation Jiuyong Li Lin Liu Ziqi Xu Ha Xuan Tran T. Le Jixue Liu CML 12 0 0 10 Apr 2023
Doubly Robust Counterfactual Classification K. Kim Edward H. Kennedy J. Zubizarreta OffRL 19 5 0 15 Jan 2023
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work? Susanne Dandl Torsten Hothorn H. Seibold Erik Sverdrup Stefan Wager A. Zeileis CML 34 11 0 21 Jun 2022
Individual causal effects from observational longitudinal studies with time-varying exposures Richard Post Z. Zhan Edwin R. van den Heuvel CML 16 0 0 06 Sep 2021
The Bias-Variance Tradeoff of Doubly Robust Estimator with Targeted $L_1$ regularized Neural Networks Predictions M. Rostami O. Saarela M. Escobar 27 1 0 02 Aug 2021
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review A. Caron G. Baio I. Manolopoulou CML 10 52 0 14 Sep 2020
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes Ahmed Alaa M. Schaar CML 17 298 0 10 Apr 2017
Random Forest Missing Data Algorithms Fei Tang H. Ishwaran 48 520 0 19 Jan 2017