نتایج جستجو برای: causal networks

تعداد نتایج: 487531  

Journal: :journal of biostatistics and epidemiology 0
kazem mohammad department of epidemiology and biostatistics, school of public health, tehran university of medical sciences, tehran, iran seyed saeed hashemi-nazari safety promotion and injury prevention research center and department of epidemiology, school of public health, shahid beheshti university of medical sciences, tehran, iran nasrin mansournia department of endocrinology, school of medicine, aja university of medical sciences, tehran, iran mohammadali mansournia department of epidemiology and biostatistics, school of public health, tehran university of medical sciences, tehran, iran

conditional  methods  of adjustment  are often used to quantify  the effect  of the exposure on the outcome.  as  a  result,  the  stratums-specific  risk  ratio  estimates  are  reported  in  the  presence  of interaction   between   exposure  and  confounder(s)   in  the  literature,  even  if  the  target  of  the intervention on the exposure is the total population and the interaction itsel...

2017
Steven M. Hill Nicole K. Nesser Katie Johnson-Camacho Mara Jeffress Aimee Johnson Chris Boniface Simon E.F. Spencer Yiling Lu Laura M. Heiser Yancey Lawrence Nupur T. Pande James E. Korkola Joe W. Gray Gordon B. Mills Sach Mukherjee Paul T. Spellman

Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual frame...

2009
Guoliang Li Tze-Yun Leong

Causal knowledge is crucial for facilitating comprehension, diagnosis, prediction, and control in automated reasoning. Active learning in causal Bayesian networks involves interventions by manipulating specific variables, and observing the patterns of change over other variables to derive causal knowledge. In this paper, we propose a new active learning approach that supports interventions with...

Journal: :J. Complex Networks 2015
James R. Clough Jamie Gollings Tamar V. Loach Tim S. Evans

In many complex networks the vertices are ordered in time, and edges represent causal connections. We propose methods of analysing such directed acyclic graphs taking into account the constraints of causality and highlighting the causal structure. We illustrate our approach using citation networks formed from academic papers, patents, and US Supreme Court verdicts. We show how transitive reduct...

2007
Salem Benferhat Salma Smaoui

This paper contains two important contributions for the development of possibilistic causal networks. The first one concerns the representation of interventions in possibilistic networks. We provide the counterpart of the ”DO” operator, recently introduced by Pearl, in possibility theory framework. We then show that interventions can equivalently be represented in different ways in possibilisti...

ژورنال: علوم آب و خاک 2008
شاهرودی , علی‌اصغر , چیذری, محمد ,

  The purpose of this study was to investigate and analyze the factors affecting farmers’ attitudinal dimensions toward participation in Water Users’ Association (WUA) by comparing two groups of farmers in irrigation networks with WUA and without it. The methodological approach was a descriptive-correlational and causal-comparative study of the survey type. The target population in the study co...

Journal: :CoRR 2011
Scott B. Morris Doug Cork Richard E. Neapolitan

There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn­ ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern­ ing how the presence of one cause of an effect makes an­...

Journal: :Int. J. Intell. Syst. 2015
Peter J. F. Lucas Arjen Hommersom

The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modeled by means of causal-independence theory, as a theory of continuous causal independe...

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