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.
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by Uzma Hasan,E... lúc arxiv.org 03-14-2024
https://arxiv.org/pdf/2303.15027.pdfYêu cầu sâu hơn