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| Autori principali: | , , |
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| Natura: | Preprint |
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2024
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| Accesso online: | https://arxiv.org/abs/2403.11219 |
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| _version_ | 1866910370577252352 |
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| author | Weinberg, Abraham Itzhak Premebida, Cristiano Faria, Diego Resende |
| author_facet | Weinberg, Abraham Itzhak Premebida, Cristiano Faria, Diego Resende |
| contents | Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11219 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causality from Bottom to Top: A Survey Weinberg, Abraham Itzhak Premebida, Cristiano Faria, Diego Resende Artificial Intelligence Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions. |
| title | Causality from Bottom to Top: A Survey |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.11219 |