Beyond Structural Causal Models: Causal Constraints Models
| Authors | |
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| Publication date | 2019 |
| Host editors |
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| 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 |
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| 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.
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| 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 |
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(Accepted author manuscript)
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