# Estimating Treatment Effects with Causal Forests: An Application

## TL;DR

This paper demonstrates the application of causal forests to estimate treatment effects using data from the National Study of Learning Mindsets, addressing practical challenges like confounding and clustered errors.

## Contribution

It provides an applied analysis of causal forests, highlighting their robustness to confounding and data clustering in real-world datasets.

## Key findings

- Causal forests effectively handle confounding via propensity scores.
- They manage clustered errors in observational data.
- Application to educational data shows practical utility.

## 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.