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. Datadriven 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 of science, cultural evolution, and computational methods. He is currently focused on the application of deep learning to network and causal inference problems to help identify how we can make science more equitable, efficient, and productive.Pablo Geraldo is a Ph.D. student in Sociology at UCLA and student affiliate of the California Center for Population Research (CCPR). His research examines inequality in education and the labor market, using a mixture of causal inference, network analysis, and machine learning approaches.