Abstract
Causality is a fundamental part of reasoning to model the physics of an application domain, to understand the behaviour of an agent or to identify the relationship between two entities. Causality occurs when an action is taken and may also occur when two happenings come undeniably together. The study of causal inference aims at uncovering causal dependencies among observed data and to come up with automated methods to find such dependencies. While there exist a broad range of principles and approaches involved in causal inference, in this position paper we argue that it is possible to unify different causality views under a common framework of symbolic learning.
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Acknowledgements
This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jiménez by the RYC15/18009, both programs funded by the Spanish government.
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Onaindia, E., Aineto, D. & Jiménez, S. A common framework for learning causality. Prog Artif Intell 7, 351–357 (2018). https://doi.org/10.1007/s13748-018-0151-y
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DOI: https://doi.org/10.1007/s13748-018-0151-y