6 papers · 1 filter
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
Fabrizio Russo, Mark Somers
The Relativity of Causal Knowledge (RCK) explains how a network of agents with different structural causal models can exchange causal knowledge through a shared interventionally co…
Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
Zihao Li, Fabrizio Russo
Causal discovery seeks to uncover causal relations from data, typically represented as causal graphs, and is essential for predicting the effects of interventions. While expert kno…
Heterogeneous Graph Neural Networks for Assumption-Based Argumentation
Preesha Gehlot, Anna Rapberger, Fabrizio Russo +1
Assumption-Based Argumentation (ABA) is a powerful structured argumentation formalism, but exact computation of extensions under stable semantics is intractable for large framework…
On Gradual Semantics for Assumption-Based Argumentation
Anna Rapberger, Fabrizio Russo, Antonio Rago +1
In computational argumentation, gradual semantics are fine-grained alternatives to extension-based and labelling-based semantics . They ascribe a dialectical strength to (component…
Argumentative Causal Discovery
Fabrizio Russo, Anna Rapberger, Francesca Toni
Causal discovery amounts to unearthing causal relationships amongst features in data. It is a crucial companion to causal inference, necessary to build scientific knowledge without…
Contestable AI needs Computational Argumentation
Francesco Leofante, Hamed Ayoobi, Adam Dejl +10
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable. Instead, contestability is advocated by AI…