Star-causality and factor analysis: old stories and new perspectives View Full Text


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Article Info

DATE

2017-12

AUTHORS

Lei Xu

ABSTRACT

Advances in causal discovery from data are becoming a widespread topic in machine learning these recent years. In this paper, studies on conditional independence-based causality are briefly reviewed along a line of observable two-variable, three-variable, star decomposable, and tree decomposable, as well as their relationship to factor analysis. Then, developments along this line are further addressed from three perspectives with a number of issues, especially on learning approximate star decomposable, and tree decomposable, as well as their generalisations to block star-causality analysis on factor analysis and block tree decomposable analysis on linear causal model. More... »

PAGES

17

References to SciGraph publications

  • 1993. Causation, Prediction, and Search in NONE
  • 2011. Rubin Causal Model in INTERNATIONAL ENCYCLOPEDIA OF STATISTICAL SCIENCE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s40535-017-0046-1

    DOI

    http://dx.doi.org/10.1186/s40535-017-0046-1

    DIMENSIONS

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