Abstract
Structural equation modeling (SEM) is being applied to ever more complex data types and questions, often requiring extensions such as regularization or novel fitting functions. To extend SEM, researchers currently need to completely reformulate SEM and its optimization algorithm–a challenging and time–consuming task. In this paper, we introduce the computation graph for SEM, and show that this approach can extend SEM without the need for bespoke software development. We show that both existing and novel SEM improvements follow naturally. To demonstrate, we introduce three SEM extensions: least absolute deviation estimation, Bayesian LASSO optimization, and sparse high–dimensional mediation analysis. We provide an implementation of SEM in PyTorch–popular software in the machine learning community–to accelerate development of structural equation models adequate for modern–day data and research questions.
| Original language | English |
|---|---|
| Pages (from-to) | 233-247 |
| Number of pages | 15 |
| Journal | Structural Equation Modeling |
| Volume | 29 |
| Issue number | 2 |
| Early online date | 20 Oct 2021 |
| DOIs | |
| Publication status | Published - 4 Mar 2022 |
Bibliographical note
Funding Information:This work was supported by The Netherlands Organization for Scientific Research (NWO) under grant number [406.17.057]. We thank Rogier Kievit and Laura Boeschoten for their comments on earlier versions of this manuscript and Maksim Rudnev for his helpful questions regarding our software.
Publisher Copyright:
© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.
Keywords
- Structural equation modeling
- computation graphs
- deep learning
- optimization
- regularization