Estimating Treatment Effects with Causal Forests: An Application

By Susan Athey, Stefan Wager. Observational Studies, 2019

510 citations recorded by the source corpus.

Abstract:We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and discusses resulting practical and conceptual challenges. 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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ee07ca60a0619f2f0cb72c7fc690c14a6598801e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Stefan Wager
2. Susan Athey
3. Guido W. Imbens
4. Mihaela van der Schaar
5. Ahmed M. Alaa
6. Nathan Kallus
7. Max Tabord‐Meehan
8. Richard M. Shiffrin

In Statistics and Probability

1. Susan Athey
2. Stefan Wager
3. Mihaela van der Schaar
4. Guido W. Imbens
5. Ahmed M. Alaa
6. Victor Chernozhukov
7. David A. Hirshberg
8. Vira Semenova

Citations in the corpus

• Policy Learning With Observational Data (2017, methodology): “However, we caution that our formal results only apply to methods that solve the problem (38) exactly; see Wager (2019) for further discussion. ducted a randomized study to evaluate the program.”
• Efficient Policy Learning (2017, methodology): “17Random forests are a type of adaptive nearest neighbor estimator that use an ensemble of trees to define a relevant neighborhood function for each query point; see Athey and Wager (2019) for a discussion of random forests in the context of doubly robust methods for causal inference.”