Beyond Interaction Effects: Two Logics for Studying Population Inequalities
This work addresses a methodological challenge for sociologists and social scientists in inequality research, providing a framework to navigate between deductive and inductive approaches, but it is incremental as it builds on existing methods without introducing new paradigms.
The paper tackles the problem of choosing between deductive interaction models and inductive machine learning methods for studying population inequalities, demonstrating through simulation when each approach excels based on a tradeoff between interpretability and flexibility.
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.