Argumentation-Based Probabilistic Causal 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

We introduce an argumentation-based approach for conducting probabilistic causal reasoning. For that, we consider Pearl’s causal models where causal relations are modelled via structural equations and a probability distribution over background atoms. The probability that some causal statement holds is then computed by constructing a probabilistic argumentation framework and determining its extensions. This framework can then be used to generate argumentative explanations for the (non-)acceptance of the causal statement. Furthermore, we present an argumentation-based version of the twin network method for dealing with counterfactuals. Finally, we show that our approach yields the same results for causal and counterfactual queries as Pearl’s model.
Original languageEnglish
Title of host publicationRobust Argumentation Machines - First International Conference, RATIO 2024, Proceedings
EditorsPhilipp Cimiano, Anette Frank, Michael Kohlhase, Benno Stein
PublisherSpringer Verlag
Pages221-236
Number of pages16
Volume14638 LNAI
ISBN (Print)9783031635359
DOIs
Publication statusPublished - 2024
Event1st International Conference on Robust Argumentation Machines, RATIO 2024 - Bielefeld, Germany
Duration: 5 Jun 20247 Jun 2024
https://ratio-conference.net/

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14638 LNAI
ISSN0302-9743

Conference

Conference1st International Conference on Robust Argumentation Machines, RATIO 2024
Abbreviated titleRATIO 2024
Country/TerritoryGermany
CityBielefeld
Period5/06/247/06/24
Internet address

Keywords

  • argumentation
  • causality
  • counterfactuals

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