Causality from Bottom to Top: A Survey

TL;DR

This survey reviews the development and application of causality over five decades, highlighting its integration with AI and its role in explainability across diverse fields.

Contribution

It provides a comprehensive overview of causality's evolution, its interaction with modern AI techniques, and discusses evaluation methods and future research directions.

Findings

Causality enhances explainability and trustworthiness of models.

It interacts significantly with AI, including GAI and deep learning.

Causality's impact spans multiple disciplines, improving decision-making.

Abstract

Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded 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 the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.