Erschienen in:
Robust Argumentation Machines (2024), Seite 221-236
Sprache:
Englisch
DOI:
10.1007/978-3-031-63536-6_13
ISBN:
9783031635366;
9783031635359
ISSN:
0302-9743;
1611-3349
Entstehung:
Anmerkungen:
Beschreibung:
AbstractWe 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.