Evaluation of Causal Structure Learning Algorithms via Risk Estimation
METADATA ONLY
Loading...
Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
Abstract
Recent years have seen many advances in methods for causal structure learning from data. The empirical assessment of such methods, however, is much less developed. Motivated by this gap, we pose the following question: how can one assess, in a given problem setting, the practical efficacy of one or more causal structure learning methods? We formalize the problem in a decision-theoretic framework, via a notion of expected loss or risk for the causal setting. We introduce a theoretical notion of causal risk as well as sample quantities that can be computed from data, and study the relationship between the two, both theoretically and through an extensive simulation study. Our results provide an assumptions-light framework for assessing causal structure learning methods that can be applied in a range of practical use-cases.
Permanent link
Publication status
published
Book title
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)
Journal / series
Volume
124
Pages / Article No.
151 - 160
Publisher
PMLR
Event
36th Conference on Uncertainty in Artificial Intelligence (UAI 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03789 - Maathuis, Marloes (ehemalig) / Maathuis, Marloes (former)
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
Funding
172603 - Causal learning in complex systems (SNF)