Causal Interaction in Bayesian Networks
نویسندگان
چکیده
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 considerable interest, philosophy has provided no useful account of causal interaction. Here we provide one, employing the concepts of Bayesian networks. With it we demonstrate the failure of one of philosophy’s more sophisticated attempts to deal with the concept of causal interaction, that of Ellery Eells’ Probabilistic Causality (1991).
منابع مشابه
An Introduction to Inference and Learning in Bayesian Networks
Bayesian networks (BNs) are modern tools for modeling phenomena in dynamic and static systems and are used in different subjects such as disease diagnosis, weather forecasting, decision making and clustering. A BN is a graphical-probabilistic model which represents causal relations among random variables and consists of a directed acyclic graph and a set of conditional probabilities. Structure...
متن کاملBayesian Network Modelling by Qualitative Patterns
In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesian-network formalism and the features of the problem to be modelled. A notion that has been suggested in the literature to facilitate Bayesian-network development is causal independence. It allows exploiting compact representations of probabilistic ...
متن کاملBayesian network modelling through qualitative patterns
In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesian-network formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They ...
متن کاملParameter Estimation in Large Causal Models
The assessment of a probability distribution that is associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions, based on the notion of causal independence, have therefore been proposed, as these allow defining a probability distribution in terms of Boolean combinations of local distributions. In Bayesian networks which need to mo...
متن کاملRisk Analysis of Operating Room Using the Fuzzy Bayesian Network Model
To enhance Patient’s safety, we need effective methods for risk management. This work aims to propose an integrated approach to risk management for a hospital system. To improve patient’s safety, we should develop flexible methods where different aspects of risk and type of information are taken into consideration. This paper proposes a fuzzy Bayesian network to model and analyze risk in the op...
متن کامل