Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the...

Most methods for learning causal structures from non-experimental data rely on some assumptions of simplicity, the most famous of which is known as the Faithfulness condition. Without assuming such conditions to begin with, we develop a learning theory for inferring the structure of a causal Bayesian network, and we use the theory to provide a novel justification of a certain assumption of simp...

This paper addresses the problem of identifying causal effects from nonexperimental data in a causal Bayesian network, i.e., a directed acyclic graph that represents causal relationships. The identifiability question asks whether it is possible to compute the probability of some set of (effect) variables given intervention on another set of (intervention) variables, in the presence of non-obser...

This paper addresses the problem of identifying causal effects from nonex-perimental data in a causal Bayesian network, i.e., a directed acyclic graph thatrepresents causal relationships. The identifiability question asks whether itis possible to compute the probability of some set of (effect) variables givenintervention on another set of (intervention) variables, in the pre...

Utilizing Bayesian beliefnetworks as a model of causality, we examined medical students' ability to discover causal relationships from observational data. Nine sets ofpatient cases were generatedfrom relatively simple causal beliefnetworks by stochastic simulation. Twenty participants examined the data sets and attempted to discover the underlying causal relafionships. Performance was poor in g...

We address the problem of identifying dynamic sequential plans in the framework of causal Bayesian networks, and show that the problem is reduced to identifying causal effects, for which there are complete identification algorithms available in the literature.