From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research
arXiv:2607.00512v1 Announce Type: new 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 increasing complexity and opacity of AI models necessitate more robust and transparent methods for causal inference and validation in research, especially as AI applications expand into critical domains.
This development offers a more rigorous framework for validating research findings in fields like business and information systems, enhancing the reliability of conclusions drawn from survey data and complex models.
Traditional SEM findings can now be cross-validated and strengthened using DML, providing a more stable and trustworthy basis for theory development and practical application.
- · Academics and researchers
- · Data scientists
- · Business and information systems fields
- · Researchers relying solely on traditional SEM
- · Poorly validated research
Research methodologies in social sciences and business will adopt more robust, multi-method validation approaches.
Increased confidence in research findings will accelerate the adoption of AI-driven insights in critical decision-making processes.
The enhanced credibility of AI-driven research could lead to more ethical and reliable AI systems, reducing bias and improving trust in automated decision-making.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG