Estimating Treatment Effects with Causal Forests: An Application
Methodology
2019-02-21 v1
Abstract
We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges. In particular, we discuss how causal forests use estimated propensity scores to be more robust to confounding, and how they handle data with clustered errors.
Cite
@article{arxiv.1902.07409,
title = {Estimating Treatment Effects with Causal Forests: An Application},
author = {Susan Athey and Stefan Wager},
journal= {arXiv preprint arXiv:1902.07409},
year = {2019}
}
Comments
This note will appear in an upcoming issue of Observational Studies, Empirical Investigation of Methods for Heterogeneity, that compiles several analyses of the same dataset