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Erschienen in:
2017 | OriginalPaper | Buchkapitel
On the Relations of Theoretical Foundations of Different Causal Inference Algorithms
verfasst von : Furui Liu, Laiwan Chan
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017
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Abstract
Telling cause from effect attracts various attentions from researchers recently. Here we study the algorithms proposed under the postulate of independence between cause and mechanisms. Firstly, we conduct a review of the different definitions of independence in different causal inference algorithms, and show how these theories could lead to practical methodologies. Then, we provide justifications about their links, showing how the seemingly different theoretical foundations could be integrated. This provides new insights of the relations between different inference algorithms, and gives readers a comprehensive understanding of the methods under this research topic.