Measurement Dependence Inducing Latent Causal Models

Author(s)
Alex Markham, Moritz Grosse-Wentrup
Abstract

We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models. We show that thistask can be framed in terms of the graph theoretic problem of finding edge clique covers, resulting in an algorithm for returning minimalMeDIL causal models (minMCMs). This algorithm is non-parametric, requiring no assumptions about linearity or Gaussianity. Furthermore, despite rather weak assumptions aboutthe class of MeDIL causal models, we show that minimality in minMCMs implies some rather specific and interesting properties. By establishing MeDIL causal models as a semantics for edge clique covers, we also provide a starting point for future work further connecting causal structure learning to developments in graph theory and network science.

Organisation(s)
Research Group Neuroinformatics, Vienna Cognitive Science Hub, Research Platform Data Science @ Uni Vienna
Journal
Proceedings Conference on Uncertainty in Artificial Intelligence
Volume
124
No. of pages
10
Publication date
10-2019
Peer reviewed
Yes
Austrian Fields of Science 2012
102019 Machine learning, 101011 Graph theory, 101018 Statistics
Keywords
Portal url
https://ucris.univie.ac.at/portal/en/publications/measurement-dependence-inducing-latent-causal-models(3069129f-a7f6-466e-8432-4e43ef43f57a).html