Literature DB >> 26771754

The TETRAD Project: Constraint Based Aids to Causal Model Specification.

R Scheines, P Spirtes, C Glymour, C Meek, T Richardson.   

Abstract

The statistical community has brought logical rigor and mathematical precision to the problem of using data to make inferences about a model's parameter values. The TETRAD project, and related work in computer science and statistics, aims to apply those standards to the problem of using data and background knowledge to make inferences about a model's specification. We begin by drawing the analogy between parameter estimation and model specification search. We then describe how the specification of a structural equation model entails familiar constraints on the covariance matrix for all admissible values of its parameters; we survey results on the equivalence of structural equation models, and we discuss search strategies for model specification. We end by presenting several algorithms that are implemented in the TETRAD I1 program.

Year:  1998        PMID: 26771754     DOI: 10.1207/s15327906mbr3301_3

Source DB:  PubMed          Journal:  Multivariate Behav Res        ISSN: 0027-3171            Impact factor:   5.923


  15 in total

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6.  Markov Boundary Discovery with Ridge Regularized Linear Models.

Authors:  Eric V Strobl; Shyam Visweswaran
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8.  Scoring Bayesian Networks of Mixed Variables.

Authors:  Bryan Andrews; Joseph Ramsey; Gregory F Cooper
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9.  Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.

Authors:  Scott A Malec; Peng Wei; Elmer V Bernstam; Richard D Boyce; Trevor Cohen
Journal:  J Biomed Inform       Date:  2021-03-11       Impact factor: 6.317

10.  Causal Inference Gates Corticostriatal Learning.

Authors:  Hayley M Dorfman; Momchil S Tomov; Bernice Cheung; Dennis Clarke; Samuel J Gershman; Brent L Hughes
Journal:  J Neurosci       Date:  2021-07-09       Impact factor: 6.167

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