Theoretical foundations of causal forests

This section explores the theoretical foundations of causal forests, including the necessary conditions for their application, such as large sample sizes and high-dimensional covariates. It also discusses the advantages of causal forests, such as their flexibility in handling complex data and ability to capture heterogeneous effects. We also discuss their limitations, including computational complexity and challenges in interpretability. Next, we will explain when you can apply causal forests effectively.

Conditions necessary for causal forest applications

For causal forests to be appropriately applied, the following conditions are preferred:

• Large sample size: Causal forests, like other machine learning methods, benefit from large datasets to improve the accuracy and reliability of the treatment effect estimates.
• High-dimensional covariates: The method is particularly useful when dealing with high-dimensional covariates where...