Beyond Structural Causal Models: Causal Constraints Models
Proceedings of the 35th Annual Conference on Uncertainty in Artificial Intelligence  (UAI 2019
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 dynamicat 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.