Beyond Structural Causal Models: Causal Constraints Models

Open Access
Authors
Publication date 2019
Host editors
  • A. Globerson
  • R. Silva
Book title Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence
Book subtitle UAI 2019, Tel Aviv, Israel, July 22-25, 2019
Event Conference on Uncertainty in Artificial Intelligence 2019
Article number 205
Number of pages 10
Publisher Corvallis, OR: AUAI Press
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
Structural Causal Models (SCMs) provide a popular causal modeling framework. In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium. Instead, we propose a generalization of the notion of an SCM, that we call Causal Constraints Model (CCM), and prove that CCMs do capture the causal semantics of such systems. We show how CCMs can be constructed from differential equations and initial conditions and we illustrate our ideas further on a simple but ubiquitous (bio)chemical reaction. Our framework also allows to model functional laws, such as the ideal gas law, in a sensible and intuitive way.
Document type Conference contribution
Note With supplementary material
Language English
Published at http://auai.org/uai2019/proceedings/papers/205.pdf
Other links http://auai.org/uai2019/proceedings/supplements/205_supplement.pdf https://dblp.org/db/conf/uai/uai2019.html http://auai.org/uai2019/accepted.php
Downloads
UAI2019_205_joined (Accepted author manuscript)
Permalink to this page
Back