Argumentative causal discovery
File(s)
Accepted version
OA Location
Author(s)
Russo, Fabrizio
Rapberger, anna
Toni, francesca
Type
Conference Paper
Abstract
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 resorting to expensive or impossible randomised control trials.
In this paper, we explore how reasoning with symbolic representations can support causal discovery.
Specifically, we deploy assumption-based argumentation (ABA), a well-established and powerful knowledge representation formalism, in combination with causality theories, to learn graphs which reflect causal dependencies in the data.
We prove that our method exhibits desirable properties, notably that, under natural conditions, it can retrieve ground-truth causal graphs.
We also conduct experiments with an implementation of our method in answer set programming (ASP) on four datasets from standard benchmarks in causal discovery, showing that our method compares well against established baselines.
It is a crucial companion to causal inference, necessary to build scientific knowledge without resorting to expensive or impossible randomised control trials.
In this paper, we explore how reasoning with symbolic representations can support causal discovery.
Specifically, we deploy assumption-based argumentation (ABA), a well-established and powerful knowledge representation formalism, in combination with causality theories, to learn graphs which reflect causal dependencies in the data.
We prove that our method exhibits desirable properties, notably that, under natural conditions, it can retrieve ground-truth causal graphs.
We also conduct experiments with an implementation of our method in answer set programming (ASP) on four datasets from standard benchmarks in causal discovery, showing that our method compares well against established baselines.
Date Issued
2024-08-01
Date Acceptance
2024-07-25
Citation
Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning, 2024, pp.938-949
ISBN
978-1-956792-05-8
ISSN
2334-1033
Publisher
International Joint Conferences on Artificial Intelligence Organization
Start Page
938
End Page
949
Journal / Book Title
Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning
Copyright Statement
Copyright © 2024 International Joint Conferences on Artificial Intelligence Organization. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
Sponsor
Royal Academy Of Engineering
JPMorgan Chase Bank, N.A.
Commission of the European Communities
Grant Number
RCSRF2021\11\45
COLAR_P86244
101020934
Source
The 21st International Conference on Knowledge Representation and Reasoning (KR-2024)
Publication Status
Published
Start Date
2024-11-02
Finish Date
2024-11-08
Coverage Spatial
Hanoi, Vietnam