Argumentation-based Causal and Counterfactual Reasoning

Lars Bengel; Lydia Blümel; Tjitze Rienstra; Matthias Thimm

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 proceeding › Conference article in proceeding › Academic › peer-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 language	English
Title of host publication	CEUR Workshop Proceedings - 1st International Workshop on Argumentation for eXplainable AI (ArgXAI)
Number of pages	12
Volume	3209
Publication status	Published - 2022
Event	1st 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 2022 → 12 Sept 2022 https://people.cs.umu.se/tkampik/argxai/2022.html

Publication series

Series	CEUR Workshop Proceedings
Volume	3209
ISSN	1613-0073

Workshop

Workshop	1st International Workshop on Argumentation for eXplainable AI
Country/Territory	United Kingdom
City	Cardiff
Period	12/09/22 → 12/09/22
Internet address	https://people.cs.umu.se/tkampik/argxai/2022.html

Keywords

Abstract Argumentation
Causality
Counterfactuals

Access to Document

https://dblp.org/rec/conf/comma/BengelBRT22Licence: CC0

Cite this

@inproceedings{1b46e29cf7cb4602a2bdd37bbd71d5e6,

title = "Argumentation-based Causal and Counterfactual Reasoning",

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.",

keywords = "Abstract Argumentation, Causality, Counterfactuals",

author = "Lars Bengel and Lydia Bl{\"u}mel and Tjitze Rienstra and Matthias Thimm",

note = "Funding Information: Acknowledgements The research reported here was partially supported by the Deutsche Forschungsgemeinschaft (grant 375588274). Publisher Copyright: {\textcopyright} 2022 Copyright for this paper by its authors.; 1st International Workshop on Argumentation for eXplainable AI : Co-located with 9th International Conference on Computational Models of Argument (COMMA 2022) ; Conference date: 12-09-2022 Through 12-09-2022",

year = "2022",

language = "English",

volume = "3209",

series = "CEUR Workshop Proceedings",

publisher = "Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V",

booktitle = "CEUR Workshop Proceedings - 1st International Workshop on Argumentation for eXplainable AI (ArgXAI)",

url = "https://people.cs.umu.se/tkampik/argxai/2022.html",

}

Bengel, L, Blümel, L, Rienstra, T & Thimm, M 2022, Argumentation-based Causal and Counterfactual Reasoning. in CEUR Workshop Proceedings - 1st International Workshop on Argumentation for eXplainable AI (ArgXAI). vol. 3209, CEUR Workshop Proceedings, vol. 3209, 1st International Workshop on Argumentation for eXplainable AI, Cardiff, United Kingdom, 12/09/22. <https://dblp.org/rec/conf/comma/BengelBRT22>

Argumentation-based Causal and Counterfactual Reasoning. / Bengel, Lars; Blümel, Lydia; Rienstra, Tjitze et al.
CEUR Workshop Proceedings - 1st International Workshop on Argumentation for eXplainable AI (ArgXAI). Vol. 3209 2022. (CEUR Workshop Proceedings, Vol. 3209).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - Argumentation-based Causal and Counterfactual Reasoning

AU - Bengel, Lars

AU - Blümel, Lydia

AU - Rienstra, Tjitze

AU - Thimm, Matthias

N1 - Funding Information: Acknowledgements The research reported here was partially supported by the Deutsche Forschungsgemeinschaft (grant 375588274). Publisher Copyright: © 2022 Copyright for this paper by its authors.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Abstract Argumentation

KW - Causality

KW - Counterfactuals

M3 - Conference article in proceeding

VL - 3209

T3 - CEUR Workshop Proceedings

BT - CEUR Workshop Proceedings - 1st International Workshop on Argumentation for eXplainable AI (ArgXAI)

T2 - 1st International Workshop on Argumentation for eXplainable AI

Y2 - 12 September 2022 through 12 September 2022

ER -

Argumentation-based Causal and Counterfactual Reasoning

Abstract

Publication series

Workshop

Keywords

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