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Comorbid science?1

Published online by Cambridge University Press:  29 June 2010

David Danks
Affiliation:
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213. [email protected]://www.hss.cmu.edu/philosophy/[email protected]@andrew.cmu.eduhttp://www.hss.cmu.edu/philosophy/[email protected]://www.hss.cmu.edu/philosophy/faculty-scheines.php Institute for Human and Machine Cognition, Pittsburgh, PA 15213.
Stephen Fancsali
Affiliation:
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213. [email protected]://www.hss.cmu.edu/philosophy/[email protected]@andrew.cmu.eduhttp://www.hss.cmu.edu/philosophy/[email protected]://www.hss.cmu.edu/philosophy/faculty-scheines.php
Clark Glymour
Affiliation:
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213. [email protected]://www.hss.cmu.edu/philosophy/[email protected]@andrew.cmu.eduhttp://www.hss.cmu.edu/philosophy/[email protected]://www.hss.cmu.edu/philosophy/faculty-scheines.php Institute for Human and Machine Cognition, Pittsburgh, PA 15213.
Richard Scheines
Affiliation:
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213. [email protected]://www.hss.cmu.edu/philosophy/[email protected]@andrew.cmu.eduhttp://www.hss.cmu.edu/philosophy/[email protected]://www.hss.cmu.edu/philosophy/faculty-scheines.php

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.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2010

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Footnotes

1.

Authors are listed in alphabetical order; all contributed equally to this commentary.

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