Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score–based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.

个体对诸如事件或干预之类的治疗方法的反应并不统一。社会学家常规地将样本划分为亚组，以根据理论先验探索不同种族和性别等协变量对治疗效果的影响。数据驱动的发现也是很常规的，但是社会学家通常进行的分析常常是有问题的，并且很少使我们超越我们的偏见去探索新的有意义的亚群。基于决策树的新兴机器学习方法使研究人员能够探索他们以前可能未曾考虑或设想的变异源。在本文中，作者使用基于树的机器学习（即因果树）对样本进行递归划分，以发现影响异质性的来源。评估社会不平等的中心议题，在大学对工资的影响上，作者将基于协变量和倾向得分的划分方法与基于因果树的递归划分相比较。决策树虽然已被森林所取代以进行估算，但可用于发现对处理有反应的亚种群。利用观测数据，作者通过应用特定于叶的效果估计策略来调整已观察到的混淆，从而扩展了现有的因果树文献，包括倾向性加权，最近邻匹配和双重健壮的因果林。我们还评估了本地化的余额指标和敏感性分析，以解决差异不平衡和未观察到的混淆的可能性。