This paper focuses on understanding the laws governing phenomena, a key area of scientific progress, particularly in modeling causal interactions. Causal inference specializes in quantifying the underlying relationships linking cause and effect. This paper comprehensively explores recent advances in causal discovery—the process of recovering causal graphs from data to identify and estimate causal effects. It provides a coherent overview of existing algorithms developed in various settings, presents useful tools and data, and demonstrates the applicability of these methods through practical applications.
