Elia Lapenta, Anthony Strittmatter, Pedro Vergara Merino
arXiv 7 Jul 2026 · Econometrics
arXiv:2607.06412 · PDF · DOI · OpenAlex · Extracted main text
This study proposes a formal, computationally efficient nonparametric omnibus test for treatment-effect heterogeneity that is compatible with a broad class of estimators, including modern machine-learning methods. The test is designed for settings in which identification can rely on high-dimensional controls while heterogeneity is assessed with respect to a low-dimensional subset of covariates. We derive the test statistic's asymptotic null distribution and develop a bootstrap procedure that is efficient because it avoids re-estimating nuisance parameters in each iteration. The testing approach applies to multiple empirical designs, including randomized experiments, selection-on-observables, difference-in-differences, and instrumental-variables settings. Monte Carlo simulations show that the test attains near-nominal size under the null and exhibits good power against heterogeneous alternatives. We further illustrate the procedure using two empirical applications on retirement savings and trade liberalization.
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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 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 5 | 3 | 100% |
| 2 | Chang, N.-C (2020) Double/debiased machine learning for difference-in-differences models | 0.843 | 3 | 3 | 100% |
| 3 | Bierens, H. J (2016) Econometric Model Specification | 0.811 | 4 | 2 | 100% |
| 4 | Athey, S. and Imbens, G (2016) Recursive partitioning for heterogeneous causal effects | 0.644 | 2 | 2 | 100% |
| 5 | Athey, S., Tibshirani, J., and Wager, S (2019) Generalized random forests | 0.644 | 2 | 2 | 100% |
| 6 | Crump, R. K., Hotz, V. J., Imbens, G. W., and Mitnik, O. A (2008) Nonparametric tests for treatment effect heterogeneity | 0.644 | 2 | 2 | 100% |
| 7 | Ding, P., Feller, A., and Miratrix, L (2016) Randomization inference for treatment effect variation | 0.644 | 2 | 2 | 100% |
| 8 | Ding, P., Feller, A., and Miratrix, L (2019) Decomposing treatment effect variation | 0.644 | 2 | 2 | 100% |
| 9 | Fan, Q., Hsu, Y.-C., Lieli, R. P., and Zhang, Y (2022) Estimation of conditional average treatment effects with high-dimensional data | 0.644 | 2 | 2 | 100% |
| 10 | Heckman, J., Smith, J., and Clements, N (1997) Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts | 0.644 | 2 | 2 | 100% |
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