6 citations · 9 across the 10 of their papers we have counts for
11 papers
Optimal Correction Sets for Provable Redress of Contestable Causal Discovery
Fabrizio Russo, Giuseppe Mazzotta, Carmine Dodaro +2
Constraint-based causal discovery relies on conditional-independence (CI) tests that may conflict in finite samples, leaving no causal graph that satisfies them all. Causal Assumpt…
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
Avinash Kori, Fabrizio Russo
World Models (WM) are increasingly seen as a foundation for intelligent agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from…
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…
Online Handbook of Argumentation for AI: Volume 4
Lars Bengel, Lydia Blümel, Elfia Bezou-Vrakatseli +9
This volume contains revised versions of the papers selected for the fourth volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument a…
Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired Framework
Fabrizio Russo, Francesca Toni
Causal Structure Learning (CSL), also referred to as causal discovery, amounts to extracting causal relations among variables in data. CSL enables the estimation of causal effects…
Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)
Fabrizio Russo, Francesca Toni
Neural networks have proven to be effective at solving machine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature m…