Generalized Quantile Random Forest with Smoothed Estimating Equations

Code implementation of Generalized Random Forest (Athey et al., 2019.) with the application of smoothed estimating equations.

Setup

Requirements

pip install numpy pandas matplotlib scipy joblib Cython

Cloning a specific branch

# Cloning the GRF with a smoothed estimating equation
git clone --branch feature/quantile-see-forest --single-branch https://github.com/Rouxist/generalized-random-forest.git

# Cloning the GRF (fit with mean, predict quantiles)
git clone --branch feature/quantreg-forest --single-branch https://github.com/Rouxist/generalized-random-forest.git

# Cloning the GRF (fit and predict exact quantiles without approximation)
git clone --branch feature/quantile-forest --single-branch https://github.com/Rouxist/generalized-random-forest.git

Cython code Compilation

python3 setup.py build_ext --inplace

Running the model

# GRF with a smoothed estimating equation
python3 cython_test_qseegrf.py

Reference

1. Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests.
2. https://github.com/py-why/EconML/