On Discrimination Discovery Using Causal Networks

Causal gene networks model the flow of information within a cell. Reconstructing causal networks from omics data is challenging because correlation does not imply causation. When genomics and transcriptomics data...

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Identifiability and transportability in dynamic causal networks

International Journal of Data Science and Analytics ◽

10.1007/s41060-016-0028-8 ◽

2016 ◽

Vol 3 (2) ◽

pp. 131-147 ◽

Cited By ~ 2

Author(s):

Gilles Blondel ◽

Marta Arias ◽

Ricard Gavaldà

Keyword(s):

Causal Networks

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INDEPENDENCE IN COMPLETE AND INCOMPLETE CAUSAL NETWORKS UNDER MAXIMUM ENTROPY

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488508005571 ◽

2008 ◽

Vol 16 (05) ◽

pp. 699-713 ◽

Cited By ~ 1

Author(s):

MICHAEL J. MARKHAM

Keyword(s):

Expert System ◽

Probability Distribution ◽

Maximum Entropy ◽

Linear Constraints ◽

Causal Network ◽

Missing Information ◽

Causal Networks ◽

Small Set

In an expert system having a consistent set of linear constraints it is known that the Method of Tribus may be used to determine a probability distribution which exhibits maximised entropy. The method is extended here to include independence constraints (Accommodation). The paper proceeds to discusses this extension, and its limitations, then goes on to advance a technique for determining a small set of independencies which can be added to the linear constraints required in a particular representation of an expert system called a causal network, so that the Maximum Entropy and Causal Networks methodologies give matching distributions (Emulation). This technique may also be applied in cases where no initial independencies are given and the linear constraints are incomplete, in order to provide an optimal ME fill-in for the missing information.

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Perceived Causal Problem Networks: Reliability, Central Problems, and Clinical Utility for Depression

Assessment ◽

10.1177/10731911211039281 ◽

2021 ◽

pp. 107319112110392

Author(s):

Lars Klintwall ◽

Martin Bellander ◽

Matti Cervin

Keyword(s):

Clinical Utility ◽

Treatment Success ◽

Centrality Measures ◽

Retest Reliability ◽

Causal Relations ◽

Future Studies ◽

Causal Networks ◽

Novel Method ◽

The Individual ◽

Test Retest Reliability

Personalized case conceptualization is often regarded as a prerequisite for treatment success in psychotherapy for patients with comorbidity. This article presents Perceived Causal Networks, a novel method in which patients rate perceived causal relations among behavioral and emotional problems. First, 231 respondents screening positive for depression completed an online Perceived Causal Networks questionnaire. Median completion time (including repeat items to assess immediate test–retest reliability) was 22.7 minutes, and centrality measures showed excellent immediate test–retest reliability. Networks were highly idiosyncratic, but worrying and ruminating were the most central items for a third of respondents. Second, 50 psychotherapists rated the clinical utility of Perceived Causal Networks visualizations. Ninety-six percent rated the networks as clinically useful, and the information in the individual visualizations was judged to contain 47% of the information typically collected during a psychotherapy assessment phase. Future studies should individualize networks further and evaluate the validity of perceived causal relations.

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