A Compositional Approach to Causality

Abstract

Inferring causality from equation models characterizing engineering domains is important towards predicting and diagnosing system behavior. Most previous attempts in this direction have failed to recognize the key differences between equations which model physical phenomena and those that just express rationality or numerical conveniences of the designer. These different types of equations bear different causal implications among the model parameters they relate. We show how unstructured and ad hoc formulations of equation models for apparent numerical conveniences are lossy in the causal information encoding and justify the use of CML as a model formulation paradigm which retains these causal structures among model parameters by clearly separating equations corresponding to phenomena and rationality. We provide an algorithm to infer causality from the active model fragments by using the notion of PreCondition graphs.