  Individuals do not respond uniformly to treatments, events, or interventions.
Sociologists routinely partition samples into subgroups to explore how the
effects of treatments vary by covariates like race, gender, and socioeconomic
status. In so doing, analysts determine the key subpopulations based on
theoretical priors. Data-driven discoveries are also routine, yet the analyses
by which sociologists typically go about them are problematic and seldom move
us beyond our expectations, and biases, to explore new meaningful subgroups.
Emerging machine learning methods allow researchers to explore sources of
variation that they may not have previously considered, or envisaged. In this
paper, we use causal trees to recursively partition the sample and uncover
sources of treatment effect heterogeneity. We use honest estimation, splitting
the sample into a training sample to grow the tree and an estimation sample to
estimate leaf-specific effects. Assessing a central topic in the social
inequality literature, college effects on wages, we compare what we learn from
conventional approaches for exploring variation in effects to causal trees.
Given our use of observational data, we use leaf-specific matching and
sensitivity analyses to address confounding and offer interpretations of
effects based on observed and unobserved heterogeneity. We encourage
researchers to follow similar practices in their work on variation in
sociological effects.
