Quantitative sociologists frequently use simple linear functional forms to estimate associations among variables. However, there is little guidance on whether such simple functional forms correctly reflect the underlying data-generating process. Incorrect model specification can lead to misspecification bias, and a lack of scrutiny of functional forms fosters interference of researcher degrees of freedom in sociological work. In this article, I propose a framework that uses flexible machine learning (ML) methods to provide an indication of the fit potential in a dataset containing the exact same covariates as a researcher’s hypothesized model. When this ML-based fit potential strongly outperforms the researcher’s self-hypothesized functional form, it implies a lack of complexity in the latter. Advances in the field of explainable AI, like the increasingly popular Shapley values, can be used to generate understanding into the ML model such that the researcher’s original functional form can be improved accordingly. The proposed framework aims to use ML beyond solely predictive questions, helping sociologists exploit the potential of ML to identify intricate patterns in data to specify better-fitting, interpretable models. I illustrate the proposed framework using a simulation and real-world examples.

中文翻译：

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将机器学习纳入社会学模型构建

定量社会学家经常使用简单的线性函数形式来估计变量之间的关联。然而，关于这种简单的函数形式是否正确反映底层数据生成过程的指导很少。不正确的模型规范可能会导致错误规范偏差，而缺乏对功能形式的审查会加剧社会学工作中研究者自由度的干扰。在本文中，我提出了一个框架，该框架使用灵活的机器学习 (ML) 方法来指示包含与研究人员假设模型完全相同的协变量的数据集的拟合潜力。当这种基于机器学习的拟合潜力远远优于研究人员自我假设的函数形式时，这意味着后者缺乏复杂性。可解释人工智能领域的进步，例如日益流行的 Shapley 值，可用于生成对 ML 模型的理解，以便研究人员的原始函数形式可以得到相应的改进。所提出的框架旨在使用机器学习，而不仅仅是预测问题，帮助社会学家利用机器学习的潜力来识别数据中的复杂模式，以指定更适合、可解释的模型。我使用模拟和现实世界的例子来说明所提出的框架。