Using R to understand causal forests

In this code, we will begin with data generation and see, hands-on, how to build causal forests. You don’t have to apply everything from scratch; we can utilize powerful R packages and start from there.

Installing and loading necessary packages

First, we need to load the grf package. grf , which stands for generalized random forests. It is an open source R package developed by researchers at Stanford University. It provides a powerful and flexible framework for implementing various types of random forest algorithms, including causal forests, instrumental forests, and regression forests.

One of the key strengths of the grf package is its ability to handle different types of data structures, such as panel data, clustered data, and data with missing values. It also supports a wide range of statistical estimation problems, including treatment effect estimation, HTE estimation, and quantile regression. Let’s install and load the...