Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives
Atul Rawal, Adrienne Raglin, Danda B. Rawat, Brian M. Sadler, James McCoy
Abstract
Causal inference is the idea of cause and effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why,” whereas the effect describes the “what.” The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning and artificial intelligence systems have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. We include a detailed taxonomy of causal inference frameworks, methods, and evaluation. An overview of causality for security is also provided. Open challenges are detailed, and approaches for evaluating the robustness of causal inference methods are described. This article aims to provide a comprehensive survey on such studies of causality. We provide an in-depth review of causality frameworks and describe the different methods.