#causal-forest

2 notes

• Causal Forest Causal Forest is a causal-inference application of the Generalized Random Forest (GRF) proposed by Athey, Tibshirani, and Wager (2019), splitting so as to maximize the heterogeneity of treatment effects.
• Policy Trees Policy Trees, proposed by Athey & Wager (2021), are an interpretable policy-learning method.