Accession Number:

AD1182780

Title:

Causality and Machine Learning Review

Descriptive Note:

[Technical Report, Technical Report]

Corporate Author:

DEVCOM Army Research Laboratory

Personal Author(s):

• Rawal, Atul
• Raglin, Adrienne
• Rawat, Danda B.
• Sadler, Brian M.

Report Date:

2022-10-17

Pagination or Media Count:

40

Abstract:

Causal inference has likely been a part of science as long as science itself, with the idea of cause and effect having defined the fundamental sciences from Newtons laws to 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 delineated, and measures for evaluating robustness of causal inference methods are described. This report 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.

Descriptors:

• information science
• data mining
• network science
• computational science
• information systems
• information processing
• bayesian networks
• data science
• neural networks
• machine learning
• artificial intelligence
• cybersecurity
• artificial intelligence software
• causal reasoning
• computers
• knowledge management
• surveys

Subject Categories:

• Information Science
• Statistics and Probability

Distribution Statement:

[A, Approved For Public Release]