Argumentation-based Causal and Counterfactual Reasoning

Lars Bengel*, Lydia Blümel, Tjitze Rienstra, Matthias Thimm

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

In this paper we present a model for argumentative causal and counterfactual reasoning in a logical setting. Causal knowledge is represented in this system using Pearl's causal model of a set of structural equations and a set of assumptions expressed in propositional logic. Queries concerning observations or actions can be answered by constructing an argumentation framework and determining its extensions. For counterfactual queries we propose an argumentation-based implementation of the twin network method and analyse its expressiveness.
Original languageEnglish
Title of host publicationCEUR Workshop Proceedings - 1st International Workshop on Argumentation for eXplainable AI (ArgXAI)
Number of pages12
Volume3209
Publication statusPublished - 2022
Event1st International Workshop on Argumentation for eXplainable AI: Co-located with 9th International Conference on Computational Models of Argument (COMMA 2022) - Cardiff, United Kingdom
Duration: 12 Sept 202212 Sept 2022
https://people.cs.umu.se/tkampik/argxai/2022.html

Publication series

SeriesCEUR Workshop Proceedings
Volume3209
ISSN1613-0073

Workshop

Workshop1st International Workshop on Argumentation for eXplainable AI
Country/TerritoryUnited Kingdom
CityCardiff
Period12/09/2212/09/22
Internet address

Keywords

  • Abstract Argumentation
  • Causality
  • Counterfactuals

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