| | |
| | |
Stat |
Members: 3658 Articles: 2'599'751 Articles rated: 2609
05 November 2024 |
|
| | | |
|
Article overview
| |
|
A Survey of Methods, Challenges and Perspectives in Causality | Gaël Gendron
; Michael Witbrock
; Gillian Dobbie
; | Date: |
1 Feb 2023 | Abstract: | The Causality field aims to find systematic methods for uncovering
cause-effect relationships. Such methods can find applications in many research
fields, justifying a great interest in this domain. Machine Learning models
have shown success in a large variety of tasks by extracting correlation
patterns from high-dimensional data but still struggle when generalizing out of
their initial distribution. As causal engines aim to learn mechanisms that are
independent from a data distribution, combining Machine Learning with Causality
has the potential to bring benefits to the two fields. In our work, we motivate
this assumption and provide applications. We first perform an extensive
overview of the theories and methods for Causality from different perspectives.
We then provide a deeper look at the connections between Causality and Machine
Learning and describe the challenges met by the two domains. We show the early
attempts to bring the fields together and the possible perspectives for the
future. We finish by providing a large variety of applications for techniques
from Causality. | Source: | arXiv, 2302.00293 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|