Learning linear cyclic causal models with latent variables
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The paper provides a full theoretical foundation for the causal discovery procedure first presented by Eberhardt et al. (2010) by adapting the procedure to the problem of cellular network inference, applying it to the biologically realistic data of the DREAMchallenges.Abstract:
Identifying cause-effect relationships between variables of interest is a central problem in science. Given a set of experiments we describe a procedure that identifies linear models that may contain cycles and latent variables. We provide a detailed description of the model family, full proofs of the necessary and sufficient conditions for identifiability, a search algorithm that is complete, and a discussion of what can be done when the identifiability conditions are not satisfied. The algorithm is comprehensively tested in simulations, comparing it to competing algorithms in the literature. Furthermore, we adapt the procedure to the problem of cellular network inference, applying it to the biologically realistic data of the DREAMchallenges. The paper provides a full theoretical foundation for the causal discovery procedure first presented by Eberhardt et al. (2010) and Hyttinen et al. (2010).read more
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References
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Book
Structural Equations with Latent Variables
TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Book
Causation, prediction, and search
TL;DR: The authors axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models.
Journal ArticleDOI
Causation, Prediction, and Search
TL;DR: Although Testing Statistical Hypotheses of Equivalence has some weaknesses, it is a useful reference for those interested in the question of equivalence testing, particularly in biological applications.
Journal ArticleDOI
The Method of Path Coefficients
TL;DR: The Method of Path Coefficients (MPC) as discussed by the authors is a flexible means of relating the correlation coefficients between variables in a multiple system to the functional relations among them, which has been applied in quite a variety of cases.