Measurement Dependence Inducing Latent Causal Models

Measurement Dependence Inducing Latent Causal Models

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.

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Authors
  • Markham, Alex
  • Grosse-Wentrup, Moritz
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Shortfacts
Category
Journal Paper
Divisions
Neuroinformatics
Journal or Publication Title
Conference in Uncertainty in Artificial Intelligence
ISSN
2640-3498
Publisher
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), Proceedings of Machine Learning Research
Volume
124
Date
August 2020
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