Literature DB >> 20584373

Comorbid science?

David Danks1, Stephen Fancsali, Clark Glymour, Richard Scheines.   

Abstract

We agree with Cramer et al.'s goal of the discovery of causal relationships, but we argue that the authors' characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.

Mesh:

Year:  2010        PMID: 20584373     DOI: 10.1017/S0140525X10000609

Source DB:  PubMed          Journal:  Behav Brain Sci        ISSN: 0140-525X            Impact factor:   12.579


  3 in total

1.  Latent variable and network models of comorbidity: toward an empirically derived nosology.

Authors:  Nicholas R Eaton
Journal:  Soc Psychiatry Psychiatr Epidemiol       Date:  2015-01-20       Impact factor: 4.328

2.  New insights into the correlation structure of DSM-IV depression symptoms in the general population v. subsamples of depressed individuals.

Authors:  S Foster; M Mohler-Kuo
Journal:  Epidemiol Psychiatr Sci       Date:  2017-01-09       Impact factor: 6.892

3.  Node centrality measures are a poor substitute for causal inference.

Authors:  Fabian Dablander; Max Hinne
Journal:  Sci Rep       Date:  2019-05-02       Impact factor: 4.379

  3 in total

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