Since many Decision Support Systems (DSS) in the area of causal strategy planning methods incorporate techniques to draw conclusions from an underlying model but fail to prove the implicitly assumed hypotheses within the latter, this paper focuses on the improvement of the model base quality. Therefore, this approach employs Artificial Neural Networks (ANNs) to infer the underlying causal funct...

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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...

Causal manipulation theorems proposed by Spirtes et al. and Pearl in the context of directed probabilistic graphs, such as Bayesian networks, oŒer a simple and theoretically sound formalism for predicting the eŒect of manipulation of a system from its causal model. While the theorems are applicable to a wide variety of equilibrium causal models, they do not address the issue of reversible causa...

The resting brain has been extensively investigated for low frequency synchrony between brain regions, namely Functional Connectivity (FC). However the other main stream of brain connectivity analysis that seeks causal interactions between brain regions, Effective Connectivity (EC), has been little explored. Inherent complexity of brain activities in resting-state, as observed in BOLD (Blood Ox...

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).

SFI WORKING PAPER: 2006-05-014 SFI Working Papers contain accounts of scientific work of the author(s) and do not necessarily represent the views of the Santa Fe Institute. We accept papers intended for publication in peer-reviewed journals or proceedings volumes, but not papers that have already appeared in print. Except for papers by our external faculty, papers must be based on work done at ...

Using detailed publication and citation data for over 50,000 articles from 30 major economics and finance journals, we investigate whether network proximity to an editor influences research productivity. During an editor’s tenure, his current university colleagues publish about 100% more papers in the editor’s journal, compared to years when he was not editor. In contrast to editorial nepotism,...

Dependency knowledge of the form "x is independent ofy once z is known" invariably obeys the four graphoid axioms, examples include probabilistic and database dependencies. Often, such knowledge can be represented efficiently with graphical structures such as undirected graphs and directed acyclic graphs (DAGs). In this paper we show that the graphical criterion called d-separation is a sound r...