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.
- Pub Date:
- February 2019
- Statistics - Methodology
- 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