Causal Mechanism-based Model Construction

Abstract

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).

Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)

References in corpus (3)

• Object-Oriented Bayesian Networks
• Network Fragments: Representing Knowledge for Constructing Probabilistic Models
• SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation