Harnessing Causal Forests and Machine Learning Methods

In this chapter, we will guide you through the theoretical foundations and practical implementation of causal forests using R. You will learn why causal forests are essential for causal inference, their ability to estimate heterogeneous treatment effects (HTE), and how they differ from traditional random forests. We will dig deep into the mathematical principles and mechanisms that underpin causal forests, including their use of a splitting criterion that maximizes differences in treatment effects and the concept of honest estimation. By the end of this chapter, you will understand the importance of causal forests in providing fine-tuned insights into how different subpopulations respond to interventions, setting up the necessary environment, and building and tuning these models in R. This knowledge will empower you to make informed, data-driven decisions based on reliable estimates of treatment effects.

So, let’s get...