Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models
Abstract
Measurement error is ubiquitous in many variables “latent-to-observed” (L2O) transformation from the MIIV approach and develop an equivalent graphical L2O transformation that allows applying existing graphical criteria to latent parameters in SEMs. We combine L2O transformation with graphical instrumental variable criteria to obtain an efficient algorithm for non-iterative parameter identification in SEMs with latent variables. We prove that this graphical L2O transformation with the instrumental set criterion is equivalent to the state-of-the-art MIIV approach for SEMs, and show that it can lead to novel identification strategies when combined with other graphical criteria.
Cite
Text
Ankan et al. "Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models." Artificial Intelligence and Statistics, 2023.Markdown
[Ankan et al. "Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models." Artificial Intelligence and Statistics, 2023.](https://mlanthology.org/aistats/2023/ankan2023aistats-combining/)BibTeX
@inproceedings{ankan2023aistats-combining,
title = {{Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models}},
author = {Ankan, Ankur and Wortel, Inge and Bollen, Kenneth and Textor, Johannes},
booktitle = {Artificial Intelligence and Statistics},
year = {2023},
pages = {7252-7264},
volume = {206},
url = {https://mlanthology.org/aistats/2023/ankan2023aistats-combining/}
}