Phillip Heiler, Michael C. Knaus
arXiv 2 Jul 2025 · Econometrics
arXiv:2507.01517 · PDF · DOI · OpenAlex · Extracted main text
Analysis of effect heterogeneity at the group level is standard practice in empirical treatment evaluation research. However, treatments analyzed are often aggregates of multiple underlying treatments which are themselves heterogeneous, e.g. different modules of a training program or varying exposures. In these settings, conventional approaches such as comparing (adjusted) differences-in-means across groups can produce misleading conclusions when underlying treatment propensities differ systematically between groups. This paper develops a novel decomposition framework that disentangles contributions of effect heterogeneity and qualitatively distinct components of treatment heterogeneity to observed group-level differences. We propose semiparametric debiased machine learning estimators that are robust to complex treatments and limited overlap. We revisit a widely documented gender gap in training returns of an active labor market policy. The decomposition reveals that it is almost entirely driven by women being treated differently than men and not by heterogeneous returns from identical treatments. In particular, women are disproportionately targeted towards vocational training tracks with lower unconditional returns.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Heiler, Phillip and Knaus, Michael C (2021) Effect or treatment heterogeneity? Policy evaluation with aggregated and disaggregated treatments self | 0.928 | 4 | 4 | 100% |
| 2 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/Debiased machine learning for treatment and structural parameters | 0.843 | 10 | 4 | 60% |
| 3 | Semenova, Vira and Chernozhukov, Victor (2021) Debiased machine learning of conditional average treatment effects and other causal functions | 0.843 | 3 | 3 | 100% |
| 4 | VanderWeele, Tyler J. and Hernan, Miguel A (2013) Causal inference under multiple versions of treatment | 0.843 | 3 | 3 | 100% |
| 5 | Caetano, Carolina and Caetano, Gregorio and Callaway, Brantly and Dy… (2025) Causal inference for aggregated treatment | 0.644 | 2 | 2 | 100% |
| 6 | Chernozhukov, Victor and Hansen, Christian and Kallus, Nathan and Sp… (2024) Applied Causal Inference Powered by ML and AI | 0.644 | 2 | 2 | 100% |
| 7 | Heiler, Phillip and Kazak, Ekaterina (2021) Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores self | 0.644 | 2 | 2 | 100% |
| 8 | Kock, Anders Bredahl and Preinerstorfer, David (2019) Power in high‐dimensional testing problems | 0.644 | 2 | 2 | 100% |
| 9 | Lee, Kaitlyn J. and Hubbard, Alan and Schuler, Alejandro (2024) Bridging binarization: Causal inference with dichotomized continuous exposures | 0.644 | 2 | 2 | 100% |
| 10 | Ma, Xinwei and Sasaki, Yuya and Wang, Yulong (2024) Testing limited overlap | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 72 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Causal Inference for Aggregated Treatment | 0.405 | 1 | 1 |
| 2 | Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity | 0.405 | 1 | 1 |