In this section
@inproceedings{Russo:2024:kr.2024/88,
author = {Russo, F and Rapberger, A and Toni, F},
doi = {kr.2024/88},
pages = {938--949},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
title = {Argumentative causal discovery},
url = {http://dx.doi.org/10.24963/kr.2024/88},
year = {2024}
}
TY - CPAPER
AB - 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.
AU - Russo,F
AU - Rapberger,A
AU - Toni,F
DO - kr.2024/88
EP - 949
PB - International Joint Conferences on Artificial Intelligence Organization
PY - 2024///
SN - 2334-1033
SP - 938
TI - Argumentative causal discovery
UR - http://dx.doi.org/10.24963/kr.2024/88
ER -
