Causality from Bottom to Top: A Survey

Causality has become a fundamental approach for explaining the relationshipsbetween events, phenomena, and outcomes in various fields of study. It hasinvaded various fields and applications, such as medicine, healthcare,economics, finance, fraud detection, cybersecurity, education, public policy,recommender systems, anomaly detection, robotics, control, sociology,marketing, and advertising. In this paper, we survey its development over thepast five decades, shedding light on the differences between causality andother approaches, as well as the preconditions for using it. Furthermore, thepaper illustrates how causality interacts with new approaches such asArtificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning,Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causalityon various fields, its contribution, and its interaction with state-of-the-artapproaches. Additionally, the paper exemplifies the trustworthiness andexplainability of causality models. We offer several ways to evaluate causalitymodels and discuss future directions.