Poster
Identification and Overidentification of Linear Structural Equation Models
Bryant Chen
Area 5+6+7+8 #83
Keywords: [ (Other) Probabilistic Models and Methods ] [ (Other) Regression ] [ Graphical Models ] [ Causality ]
In this paper, we address the problems of identifying linear structural equation models and discovering the constraints they imply. We first extend the half-trek criterion to cover a broader class of models and apply our extension to finding testable constraints implied by the model. We then show that any semi-Markovian linear model can be recursively decomposed into simpler sub-models, resulting in improved identification and constraint discovery power. Finally, we show that, unlike the existing methods developed for linear models, the resulting method subsumes the identification and constraint discovery algorithms for non-parametric models.
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