Machine Learning for Estimating Heterogeneous Causal Effects

By Susan Athey, Guido W. Imbens. 2015

30 citations recorded by the source corpus.

How researchers see this paper

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

1. Bin Yu
2. Tommaso Cai
3. Peter J. Bickel
4. Stefan Wager
5. Martin J. Wainwright
6. Matthew A. Taddy
7. Susan Athey
8. Peter Buhlmann

Citations in the corpus

• Metalearners for estimating heterogeneous treatment effects using machine learning (2017, methodology): “, Athey and Imbens, 2015). When used with trees, this has been called the Two-Tree estimator because it builds two separate trees: one tree to estimate the treated outcomes and a second tree to estimate the control outcomes. The CATE estimator is defined to be the difference between the two trees. W”
• A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation (2014, methodology): “This is an increasingly popular strategy (e.g., Athey and Imbens 2015), and we demonstrate that there can be considerable uncertainty associated with the resulting partitioning rules.”