比较演绎与归纳两种研究社会不平等的方法，揭示其优劣与适用场景。
Beyond Interaction Effects: Two Logics for Studying Population Inequalities
- 用演绎逻辑检验预设的种族性别调节效应，或用归纳逻辑通过算法探索未知模式
- 模拟显示：当假设明确时演绎更优；当关系复杂未知时，机器学习更有效
- 适合关注交叉性不平等、需权衡解释力与灵活性的研究者
当社会学家探究大学教育回报是否因种族和性别而异时，面临两种根本不同的研究路径选择。传统交互模型采用演绎逻辑：研究者预先设定可能调节效应的变量并加以验证；机器学习方法则采用归纳逻辑：算法在庞大的组合空间中搜索异质性模式。本文构建了一个框架，帮助研究者在两种路径间做出明智选择。我们揭示了可解释性与灵活性之间的权衡，并通过模拟展示不同方法在何种条件下表现更优。该框架对不平等研究尤为关键，因为理解处理效应如何在多重社会子群体中变化是核心议题。
原文摘要 · Abstract (English)
When sociologists and other social scientist ask whether the return to college differs by race and gender, they face a choice between two fundamentally different modes of inquiry. Traditional interaction models follow deductive logic: the researcher specifies which variables moderate effects and tests these hypotheses. Machine learning methods follow inductive logic: algorithms search across vast combinatorial spaces to discover patterns of heterogeneity. This article develops a framework for navigating between these approaches. We show that the choice between deduction and induction reflects a tradeoff between interpretability and flexibility, and we demonstrate through simulation when each approach excels. Our framework is particularly relevant for inequality research, where understanding how treatment effects vary across intersecting social subpopulation is substantively central.
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