Introduction to causal forests for causal inference

Causal forests are an extension of traditional random forests, which are machine learning methods used for classification and regression tasks. A random forest builds many decision trees, each trained on different random subsets of the data, to make predictions by voting (for classification) or averaging (for regression). A decision tree is a model that uses a tree-like structure to split the data based on feature values, leading to a decision outcome. It segments the data into branches to classify or predict outcomes by making sequential, rule-based decisions at each node. This approach is based on ensembling, which means using multiple models to generate output. By combining the predictions of multiple models, ensembling reduces the risk of overfitting and improves the model’s robustness, making it effective for handling large, high-dimensional datasets. For more information on random forests, you can refer to the paper by...