Graphical Models have been widely used for modelling causal relationships. We use causal Bayesian networks to model protein signaling networks and use the Bayesian approach to learn the network structure from mixed observational and experimental data. We compute the maximum a posteriori (MAP) network for a biological data set originally analyzed by Sachs et al. (2005).

Diagnostic knowledge is the basis of many non-Bayesian medical systems. To explore the advantages of their Bayesian counterparts, we need causal knowledge. This paper investigates how to mine causal knowledge from the diagnostic knowledge. Experiments indicate the proposed mining method works pretty well.

We use the implicitization procedure to generate polynomial equality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network with hidden variables. We show how we may reduce the complexity of the implicitization problem and make the problem tractable in certain causal Bayesian networks. We also show some preliminary results on th...

Artificial Intelligence (AI) and Philosophy of Science share a fundamental problem—that of understanding causality. Bayesian network techniques have recently been used by Judea Pearl in a new approach to understanding causality and causal processes (Pearl, 2000). Pearl’s approach has great promise, but needs to be supplemented with an explicit account of causal interaction. Thus far, despite co...

Bayesian networks are a useful tool. First, they are particularly useful for describing processes composed of locally interacting components; that is, the value of each component directly depends on the values of a relatively small number of components. Second, statistical foundations for learning Bayesian networks from observations, and computational algorithms to do so are well understood and...

There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn­ ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern­ ing how the presence of one cause of an effect makes an­...

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate ...

Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at lea...

Associationist theories of causal induction model learning as the acquisition of associative weights between cues and outcomes. An important deficit of this class of models is its insensitivity to the causal role of cues. A number of recent experimental findings have shown that human learners differentiate between cues that represent causes and cues that represent effects. Our Bayesian network ...