Summary

In this chapter, you learned that causal discovery is a useful tool within causal inference that aims to identify the underlying causal structure between various factors using observational data. We implemented representational approaches such as DAGs and SCMs to map out causal relationships. We also discussed various techniques, including constraint-based methods such as the PC algorithm, score-based methods such as GES, and hybrid approaches, each with their unique strengths and challenges.

Then, we illustrated the practical implementation in R through scenario-based problem solving, demonstrating the transformative potential of causal discovery across diverse fields such as public health and economics. We addressed challenges such as identifiability issues, confounding variables, and data quality, highlighting how causal discovery provides actionable insights, informs policy and decision-making, advances scientific understanding, reduces bias, and analyzes complex systems...