핵심 개념
Understanding causality from data is crucial for decision-making, with various algorithms available for causal discovery.
초록
The study delves into the significance of understanding causality from data through causal discovery algorithms. It discusses the importance of identifying cause-effect relationships among variables in complex systems to make informed decisions across diverse fields. The paper presents an extensive discussion on methods designed for causal discovery from both independent and identically distributed (I.I.D.) data and time series data. It covers common terminologies, algorithmic discussions, benchmark datasets, evaluation metrics, and applications of causal discovery algorithms in various domains.
Key points include:
- Introduction to Causal Discovery and its importance in decision-making.
- Overview of common terminologies used in causal discovery literature.
- Discussion on algorithms designed for causal discovery from I.I.D. and time series data.
- Evaluation of widely used causal discovery algorithms on benchmark datasets.
- Comparison of performances of different algorithms.
- Challenges and applications of causal discovery in multiple areas.
통계
arXiv:2303.15027v4 [cs.AI] 12 Mar 2024
인용구
"The ability to understand causality from data is one of the major milestones of human-level intelligence." - Uzma Hasan
"Causal graphs represent the causal relationships with directed arrows from the cause to the effect." - Pearl (2009)
"Systems without the knowledge of causal relationships often cannot make rational and informed decisions." - Marwala (2015)