Individuals do not respond uniformly to treatments, such as events or interventions. Social scientists 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 some scholars often go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. I use causal trees to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, I 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 Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments.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, I 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, and use sensitivity analyses to consider bias due to unobserved confounding.