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 (ML) and artificial intelligence (AI) 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 paper 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.

因果推理是因果关系的概念；这一基本科学领域可以应用于与牛顿定律或毁灭性的 COVID-19 大流行相关的问题空间。原因解释“为什么”，而结果描述“什么”。该领域本身涵盖了从统计学和计算机科学到经济学和哲学的众多学科。机器学习 (ML) 和人工智能 (AI) 系统的最新进展，激发了人们对从大量可用观测数据中识别和估计因果关系的新兴趣。这导致了各种新的研究，旨在提供识别和估计因果推理的新方法。我们提供了因果推理框架、方法和评估的详细分类。还提供了安全因果关系的概述。详细介绍了公开的挑战，并描述了评估因果推理方法稳健性的方法。本文旨在对此类因果关系研究进行全面的调查。我们对因果关系框架进行了深入的回顾，并描述了不同的方法。