In recent years, Bayesian networks have become highly successful tool for diagnosis, analysis, and decision making in real-world domains. We present an efficient algorithm for learning Bayes networks from data. Our approach constructs Bayesian networks by first identifying each node’s Markov blankets, then connecting nodes in a maximally consistent way. In contrast to the majority of work, whic...

The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions of causal structure may be useful to observers, they are at best a compressed description and at worse leave out critical information. However, recent research applying information theory to causal analysis has shown that the cau...

Information about the structure of a causal system can come in the form of observational data— random samples of the system’s autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people’s ability to infer causal structure from both observation and intervention, and to choose info...

Humans routinely confront the following key question which could be viewed as a probabilistic variant of the controllability problem: While faced with an uncertain environment governed by causal structures, how should they practice their autonomy by intervening on driver variables, in order to increase (or decrease) the probability of attaining their desired (or undesired) state for some target...

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...

This paper rests on several contributions. First, we introduce the notion of a consequence, which is a boolean expression that characterizes consistency-based diagnoses. Second, we introduce a basic algorithm for computing consequences when the system description is struc-tured using a causal network. We show that if the causal network has no undirected cycles, then a consequence has a linear s...

The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the work in this area has focused on traditional statistical learning paradigms, such as classification, prediction, clustering, pattern mining. In this ...

The identification and quantification of couplings between the individual components of a complex system can shed light on its hidden dynamics and provide insights about its mechanistic basis. Embodied cognition emerges and develops largely from the dynamic interactions in the coupled system formed by brain, body, and environment. A crucial problem is how to quantify the informational structure...

A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional prob­ ability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this context are not new (Whit­ taker, 1990; Buntine, 1991; Neal, 1992; Heck­ erman...

We report on a new approach to modelling and identifying dependencies within a gene regulatory cycle. In particular, we aim to learn the structure of a causal network from gene expression microarray data. We model causality in two ways: by using conditional dependence assumptions to model the independence of different causes on a common effect; and by relying on time delays between cause and ef...