  Binary treatments are often ex-post aggregates of multiple treatments or can
be disaggregated into multiple treatment versions. Thus, effects can be
heterogeneous due to either effect or treatment heterogeneity. We propose a
decomposition method that uncovers masked heterogeneity, avoids spurious
discoveries, and evaluates treatment assignment quality. The estimation and
inference procedure based on double/debiased machine learning allows for
high-dimensional confounding, many treatments and extreme propensity scores.
Our applications suggest that heterogeneous effects of smoking on birthweight
are partially due to different smoking intensities and that gender gaps in Job
Corps effectiveness are largely explained by differential selection into
vocational training.
