🤖 AI Summary
This study addresses the challenge of identifying causal graphs and estimating causal effects from observational data. We propose the first unified analytical framework that horizontally integrates major causal discovery paradigms—including constraint-based methods (e.g., PC), score-based methods (e.g., GES), functional causal models (e.g., LiNGAM, ANM, CAM, NOTEARS), and neural causal learning—while rigorously characterizing their identifiability conditions and practical applicability boundaries. Our contribution comprises: (1) a comprehensive knowledge graph covering 12 algorithmic families, 8 open-source toolkits, and applications across six domains (e.g., healthcare, economics, ecology); (2) standardized benchmark datasets, reproducible evaluation protocols, and practitioner-oriented guidelines; and (3) paradigm-level unification, formal identification boundary analysis, and an end-to-end resource ecosystem for real-world causal discovery deployment.
📝 Abstract
Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs is recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.