Work

Learning of Causal Relations

Year: 2011

Type: preprint

Abstract: . To learn about causal relations between variables just by observing samples from them, particular assumptions must be made about those variables ’ distributions. This article gives a practical description of how such a learning task can be undertaken based on different possible assumptions. Two categories of assumptions lead to different methods, constraint-based and Bayesian learning, and in each case we review both the basic ideas and some recent extensions and alternatives to them. 1

Authors: John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl

Institutions: Makerere University, Max Planck Society, Radboud University Nijmegen

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2105110781

Cites: 21

Cited by: 4

Related to: 10