A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
TL;DR: Connects causal observations, representations, and structure in a unified account of the components needed for causal world models.
Publications & Conferences
Publications
2026
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
TL;DR: Studies when private causal reports identify a shared causal backbone and what additional information is needed when they do not.
Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
TL;DR: Combines LLM-derived causal knowledge with statistical evidence through argumentation, keeping conflicts visible and inspectable.
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports
TL;DR: Formalises how agents with private causal models can identify and communicate their shared causal backbone.
Optimal Correction Sets for Argumentative Causal Discovery
TL;DR: Uses optimal correction sets to resolve inconsistent causal constraints within argumentative causal discovery.
Heterogeneous Graph Neural Networks for Assumption-Based Argumentation
TL;DR: Models assumption-based argumentation as a heterogeneous graph and learns to predict whether assumptions are accepted.
2025
On Gradual Semantics for Assumption-Based Argumentation
TL;DR: Introduces gradual semantics for assumption-based argumentation, assigning degrees of acceptability rather than only binary outcomes.
Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired Framework
TL;DR: Uses Shapley-inspired scores to make constraint-based causal discovery more robust to noisy conditional-independence evidence.
Heterogeneous Graph Neural Networks for Credulous Acceptance of Assumptions in ABA
TL;DR: Explores heterogeneous graph neural networks for predicting credulous acceptance of assumptions in ABA frameworks.
2024
Causal Discovery for Trustworthy Artificial Intelligence
TL;DR: Develops human-centred causal discovery methods that use game-theoretic ideas and argumentation to make AI explanations contestable and trustworthy.
Argumentative Causal Discovery
TL;DR: Introduces an argumentation-based causal discovery framework that reconciles conflicting statistical evidence and domain knowledge.
Contestable AI Needs Computational Argumentation
TL;DR: Argues that computational argumentation provides the dialogue, justification, and revision mechanisms needed for genuinely contestable AI.
Online Handbook of Argumentation for AI: Volume 4
TL;DR: Surveys contemporary argumentation-for-AI methods, tools, and applications in an openly accessible handbook.
2023
Causal Discovery and Knowledge Injection for Contestable Neural Networks
TL;DR: Uses causal discovery and knowledge injection to expose and revise neural-network mechanisms with expert input.
Argumentation for Interactive Causal Discovery
TL;DR: Presents an interactive causal discovery approach in which argumentation lets people inspect and challenge causal conclusions.
Explaining Classifiers' Outputs with Causal Models and Argumentation
TL;DR: Builds argumentative explanations of classifier outputs from causal models, making supporting and opposing reasons explicit.
2021
Forging Argumentative Explanations from Causal Models
TL;DR: Transforms causal models into structured argumentative explanations that make the reasons for an outcome explicit.