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 language | English |
---|---|
Title of host publication | Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings |
Editors | Philipp Cimiano, Anette Frank, Michael Kohlhase, Benno Stein |
Publisher | Springer Verlag |
Pages | 221-236 |
Number of pages | 16 |
Volume | 14638 LNAI |
ISBN (Print) | 9783031635359 |
DOIs | |
Publication status | Published - 2024 |
Event | 1st International Conference on Robust Argumentation Machines, RATIO 2024 - Bielefeld, Germany Duration: 5 Jun 2024 → 7 Jun 2024 https://ratio-conference.net/ |
Publication series
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 14638 LNAI |
ISSN | 0302-9743 |
Conference
Conference | 1st International Conference on Robust Argumentation Machines, RATIO 2024 |
---|---|
Abbreviated title | RATIO 2024 |
Country/Territory | Germany |
City | Bielefeld |
Period | 5/06/24 → 7/06/24 |
Internet address |
Keywords
- argumentation
- causality
- counterfactuals