English(EN) From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research

新框架增强了基于调查的研究的稳健性分析

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-01 06:46

本文介绍了一种分析基于调查的研究结果稳健性的新颖框架。它整合了结构方程模型（SEM）、双重机器学习（DML）和普通最小二乘（OLS）回归，以评估不同估计技术下关系保持的稳定性。该方法论在金融科技数字客户亲密度模型上进行了演示，帮助研究人员识别哪些发现得到了持续支持，哪些需要更谨慎的解释。 AI

影响为验证基于调查的研究结果提供了一种更严谨的方法，可能增加对机器学习技术衍生结果的信心。

排序理由该项目是一篇学术论文，详细介绍了一种新的统计分析方法。 [lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Ranga Chimhundu · 2026-07-01 06:46

从结构方程模型到双重机器学习：基于调查的研究的稳健性分析

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…
arXiv stat.ML TIER_1 English(EN) · Ka Ching Chan, Qiana Liu, Sanjib Tiwari, Ranga Chimhundu · 2026-07-02 04:00

从结构方程模型到双重机器学习：基于调查的研究的稳健性分析

arXiv:2607.00512v1 Announce Type: cross 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…