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Information Theoretic Causal Effect Quantification

Wieczorek, Aleksander and Roth, Volker. (2019) Information Theoretic Causal Effect Quantification. Entropy, 21 (10). p. 975.

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Official URL: https://edoc.unibas.ch/73940/

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Abstract

Modelling causal relationships has become popular across various disciplines. Most common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) and the Neyman-Rubin potential outcome framework. In this paper, we propose an information theoretic framework for causal effect quantification. To this end, we formulate a two step causal deduction procedure in the Pearl and Rubin frameworks and introduce its equivalent which uses information theoretic terms only. The first step of the procedure consists of ensuring no confounding or finding an adjustment set with directed information. In the second step, the causal effect is quantified. We subsequently unify previous definitions of directed information present in the literature and clarify the confusion surrounding them. We also motivate using chain graphs for directed information in time series and extend our approach to chain graphs. The proposed approach serves as a translation between causality modelling and information theory.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Biomedical Data Analysis (Roth)
UniBasel Contributors:Roth, Volker and Wieczorek, Aleksander
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:MDPI
e-ISSN:1099-4300
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:02 Jul 2020 11:55
Deposited On:24 Jun 2020 14:08

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