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Binary treatments are often ex-post aggregates of multiple treatments or canbe disaggregated into multiple treatment versions. Thus, effects can beheterogeneous due to either effect or treatment heterogeneity. We propose adecomposition method that uncovers masked heterogeneity, avoids spuriousdiscoveries, and evaluates treatment assignment quality. The estimation andinference procedure based on double/debiased machine learning allows forhigh-dimensional confounding, many treatments and extreme propensity scores.Our applications suggest that heterogeneous effects of smoking on birthweightare partially due to different smoking intensities and that gender gaps in JobCorps effectiveness are largely explained by differential selection intovocational training.

Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments