From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research
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Computer Science > Machine Learning
arXiv:2607.00512 (cs)
[Submitted on 1 Jul 2026]
Title:From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research
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Abstract:Structural equation modelling (SEM) is widely used in survey-based business and information systems research to assess latent constructs and theory-driven structural relationships. However, SEM path significance is obtained within a particular model specification and may not show whether findings remain stable under alternative estimation frameworks. This study develops and demonstrates a staged robustness analysis framework that connects SEM, ordinary least squares (OLS) regression, and Double Machine Learning (DML). SEM is first used to refine the measurement structure and estimate the robustness-baseline SEM model, in which the full theory-specified structural path system is retained for downstream robustness analysis before final structural path evaluation. OLS regression is then applied to SEM-derived construct scores as a transparent regression benchmark. Finally, DML-style residualisation is used to examine whether each tested focal relationship remains stable after flexible machine-learning-based adjustment for observed controls. Learner-sensitivity checks compare Random Forest, Gradient Boosting, and Support Vector Machine learners, and selected reverse-direction diagnostics are used to examine directional sensitivity. The framework is demonstrated using a FinTech Digital Customer Intimacy survey model. The findings identify which relationships are stable across SEM, OLS, and DML-style checks, and which require more cautious interpretation. A reproducible Google Colab workbook and generated result files are publicly available, providing a reusable template that researchers and students can adapt to other survey-based latent-construct studies. The paper contributes a practical robustness workflow and interpretation guide for survey-based researchers seeking to complement SEM with conventional and machine-learning-based robustness checks.
| Comments: | 21 pages, 1 figure, 13 tables |
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| MSC classes: | 62-04 |
| ACM classes: | H.1.0 |
| Cite as: | arXiv:2607.00512 [cs.LG] |
| (or arXiv:2607.00512v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.00512
arXiv-issued DOI via DataCite (pending registration)
|
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