Causal Mechanism-based Model Construction
This work addresses the need for more intuitive and manipulable causal models for users in fields like data analysis or decision support, but it appears incremental as it builds on existing tools like SMILE and GeNIe.
The authors tackled the problem of building graphical causal models by proposing a framework based on causal mechanisms, which are intuitive for users and support manipulation effect prediction, and they implemented it as an interactive module called ImaGeNIe in SMILE and GeNIe.
We propose a framework for building graphical causal model that is based on the concept of causal mechanisms. Causal models are intuitive for human users and, more importantly, support the prediction of the effect of manipulation. We describe an implementation of the proposed framework as an interactive model construction module, ImaGeNIe, in SMILE (Structural Modeling, Inference, and Learning Engine) and in GeNIe (SMILE's Windows user interface).