David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
We describe anytime search procedures that (1) ﬁnd disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause, if such exists; (2) return information about the causal relations among the latent factors so identiﬁed. We prove the procedure is point-wise consistent assuming (a) the causal relations can be represented by a directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption; (b) unrecorded variables are not caused by recorded variables; and (c) dependencies are linear. We compare the procedure with standard approaches over a variety of simulated structures and sample sizes, and illustrate its practical value with brief studies of social science data sets. Finally, we consider generalizations for non-linear systems. Keywords: latent variable models, causality, graphical models..
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Peter Spirtes, Christopher Meek & Thomas Richardson, Causal Inference in the Presence of Latent Variables and Selection Bias.
Peter Spirtes, Discovering Causal Relations Among Latent Variables in Directed Acyclical Graphical Models.
Jiji Zhang & Peter Spirtes, A Transformational Characterization of Markov Equivalence for Directed Maximal Ancestral Graphs.
Peter Spirtes (2005). Graphical Models, Causal Inference, and Econometric Models. Journal of Economic Methodology 12 (1):3-34.
Added to index2009-01-28
Total downloads6 ( #230,243 of 1,410,127 )
Recent downloads (6 months)1 ( #177,743 of 1,410,127 )
How can I increase my downloads?