This paper relates our experience in developing a mechanism for reasoning about the di erential diagnosis of cases involving the symptoms of heart failure using a causal model of the cardiovascular hemodynamics with probabilities relating cause to e ect. Since the problem requires the determination of causal mechanism as well as primary cause, the model has many intermediate nodes as well as ca...

In clinical trials with significant noncompliance the standard intention-to-treat analyses sometimes mislead. Rubin’s causal model provides an alternative method of analysis that can shed extra light on clinical trial data. Formulating the Rubin Causal Model as a graphical model facilitates model communication and computation.

Probabilistic graphical models are useful tools for modeling systems governed by probabilistic structure. Bayesian networks are one class of probabilistic graphical model that have proven useful for characterizing both formal systems and for reasoning with those systems. Probabilistic dependencies in Bayesian networks are graphically expressed in terms of directed links from parents to their ch...

The main goal of this paper is to describe a new graphical structure called ÔBayesian causal mapsÕ to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expertÕs cognition. It is also a Bayesian network, i.e., a graphical representation of an expertÕs knowledge based on probability theory. Bayesian causal maps enh...

The causal Bayesian approach is based on the assumption that effects (e.g., symptoms) that are not conditionally independent with respect to some causal agent (e.g., a disease) are conditionally independent with respect to some intermediate state caused by the agent, (e.g., a pathological condition). This paper describes the development of a causal Bayesian model for the diagnosis of appendicit...

We address the problem of learning grounded causal models: systems of concepts that are connected by causal relations and explicitly grounded in perception. We present a Bayesian framework for learning these models—both a causal Bayesian network structure over variables and the consequential region of each variable in perceptual space—from dynamic perceptual evidence. Using a novel experimental...

Improving venture capitalists’ decision processes is key to reducing failure rates for venture capital backed companies and to improving portfolio returns. In this paper we describe the use of a novel technique—Bayesian causal maps—to support and improve venture capital decision making. We combine causal mapping and Bayesian network techniques to construct a Bayesian causal map. The resulting p...