Implementing Causal Discovery in R

In this chapter, we’ll explore a new topic known as causal discovery. In causality, this methodology aims to learn the underlying causal structure between various factors. As we learn the basics and applications of this technique, we’ll go back to the good old representational approaches such as directed acyclic graphs (DAGs) and structural causal models (SCMs), which will serve as our tools to map out causal relationships. Furthermore, we plan to dive deep into various causal discovery techniques, including constraint-based methods such as the PC algorithm, score-based methods such as Greedy Equivalence Search (GES), and other hybrid approaches, each offering unique strengths and challenges. Implementing this practically in R with scenario-based problem solving will illustrate the transformative potential of causal discovery across diverse fields, from public health to economics. By addressing challenges such as identifiability issues...