Summary of Causality From Bottom to Top: a Survey, by Abraham Itzhak Weinberg et al.
Causality from Bottom to Top: A Survey
by Abraham Itzhak Weinberg, Cristiano Premebida, Diego Resende Faria
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive survey of causality over the past five decades is presented in this research paper, examining its differences from other approaches and preconditions for usage. The study delves into how causality interacts with AI, GAI, machine learning, deep learning, reinforcement learning, and fuzzy logic in various fields such as medicine, healthcare, economics, finance, and more. The authors also discuss the impact of causality on these fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper explores the trustworthiness and explainability of causality models and offers several methods for evaluating them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Causality is an important way to understand how things are connected. It’s used in many areas like medicine, education, and business. This paper looks back at how causality has developed over the past 50 years and compares it to other approaches. It also shows how causality works with new technologies like AI and machine learning. The authors want us to know that causality is a reliable way to understand relationships and can be used in many fields. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Reinforcement learning
