Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments - Phillip Heiler; Michael C. Knaus (Institute of Labor Economics (IZA), 2022)
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 differences in vocational training.
Keywords: C14; C21; 330; causal inference; causal machine learning; double machine learning; heterogeneous treatment effects; overlap; treatment versions