On Discrimination Discovery Using Causal Networks

Author(s):  
Lu Zhang ◽  
Yongkai Wu ◽  
Xintao Wu
2012 ◽  
Author(s):  
Reid Hastie ◽  
Benjamin M. Rottman
Keyword(s):  

2021 ◽  
Author(s):  
Adriaan-Alexander Ludl ◽  
Tom Michoel

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...


2016 ◽  
Vol 3 (2) ◽  
pp. 131-147 ◽  
Author(s):  
Gilles Blondel ◽  
Marta Arias ◽  
Ricard Gavaldà
Keyword(s):  

Author(s):  
MICHAEL J. MARKHAM

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.


Assessment ◽  
2021 ◽  
pp. 107319112110392
Author(s):  
Lars Klintwall ◽  
Martin Bellander ◽  
Matti Cervin

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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