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 …

The paper regards the identification of constraints in linear structural equation models (SEMs). It builds on existing work on half-trek criterion and recursive c-component decomposition.

Aug 7, 2015 · In this paper, we address the problem of identifying linear structural equation models. We first extend the edge set half-trek criterion to cover a broader class of models.

This paper provides an algorithm for the identification of causal parameters in linear structural models that subsumes previous state-of-the-art methods and builds on a graph-theoretic characterization of …

Using simple and direct language, this concise text provides practical guidance on a wide range of modeling methods and techniques for use with quantitative data.

Feb 1, 2025 · In this paper we study structural equation models where the relationships between the random variables are specified by a system of equations which comes from the underlying graph.

Structural equation models are multivariate statistical models that are de-fined by specifying noisy functional relationships among random variables. We consider the classical case of linear …

Dec 11, 2016 · We developed a novel approach to identification and model testing in linear structural equation models (SEMs) based on auxiliary variables (AVs), which generalizes a widely-used family …

Bibliographic details on Identification and Overidentification of Linear Structural Equation Models.

Elias Bareinboim 2 effect is identified requires modeling the underlying causal structure, which is generally done using structural equa-tion models (SEMs) (also called structural causal models) …

In this paper, we address the problems of identifying linear structural equation models and discover-ing the constraints they imply. We rst extend the half-trek criterion to cover a broader class of models …

We developed a novel approach to identifica-tion and model testing in linear structural equa-tion models (SEMs) based on auxiliary variables (AVs), which generalizes a widely-used family of methods …

The goal of this paper is to provide a succinct and relatively self-contained expo- sition of the identiability theory of a class of latent variable models called inde- pendent component analysis, as well as of a …

Structural equation models are multivariate statistical models that are de? fined by specifying noisy functional relationships among random variables. We consider the classical case of linear …

usal parameters can be uniquely es-timated from an observational, non-causal covari-ance matrix. In this paper, we provide an algorithm for the identification o. causal parameters in lin-ear structural …