A software system for causal reasoning in causal Bayesian networks

Knowing the cause and effect is important to researchers who are interested in modeling the effects of actions, and Artificial Intelligence researchers are among them. One commonly used method for modeling cause and effect is graphical model. Bayesian Network is a probabilistic graphical model for representing and reasoning uncertain knowledge. It has been used as a fundamental tool and is becoming a more and more important area for research and application in the AI field. A common graphical causal model used by many researchers in AI field is a directed acyclic graph (DAG) with causal interpretation known as the causal Bayesian network (BN). Causal reasoning and causal understanding are the causal interpretation part of a causal Bayesian Network. They enable people to find meaningful order in events that might otherwise appear random and chaotic. Further more, they can even help people to plan and predict the future. In this thesis, we develop a software system, which is a set of tools to solve causal reasoning problems, such as to identify unconditional causal effects, to identify conditional causal effects and to find constraints in a causal Bayesian Networks with hidden variables. The features of the software system are presented in detail and the applications of the software system are discussed.