arXiv 6 Jun 2023 · Econometrics
arXiv:2306.03363 · PDF · DOI · OpenAlex · Extracted main text
Experimenters often collect baseline data to study heterogeneity. I propose the first valid confidence intervals for the VCATE, the treatment effect variance explained by observables. Conventional approaches yield incorrect coverage when the VCATE is zero. As a result, practitioners could be prone to detect heterogeneity even when none exists. The reason why coverage worsens at the boundary is that all efficient estimators have a locally-degenerate influence function and may not be asymptotically normal. I solve the problem for a broad class of multistep estimators with a predictive first stage. My confidence intervals account for higher-order terms in the limiting distribution and are fast to compute. I also find new connections between the VCATE and the problem of deciding whom to treat. The gains of targeting treatment are (sharply) bounded by half the square root of the VCATE. Finally, I document excellent performance in simulation and reanalyze an experiment from Malawi.
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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 | Dizon-Ross, R (2019) Parents' beliefs about their children's academic ability: Implications for educational investments | 1.000 | 9 | 3 | 100% |
| 2 | Chernozhukov, V., Demirer, M., Duflo, E., Fernández-Val, I (2022) a | 0.874 | 12 | 2 | 100% |
| 3 | Ding, P., Feller, A., Miratrix, L (2019) Decomposing treatment effect variation | 0.874 | 6 | 2 | 100% |
| 4 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 0.874 | 5 | 2 | 100% |
| 5 | Levy, J., van der Laan, M., Hubbard, A., Pirracchio, R (2021) A fundamental measure of treatment effect heterogeneity | 0.874 | 5 | 2 | 100% |
| 6 | Kitagawa, T., Tetenov, A (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.843 | 4 | 3 | 75% |
| 7 | Belloni, A., Chernozhukov, V., Hansen, C (2014) Inference on treatment effects after selection among high-dimensional controls | 0.843 | 3 | 3 | 100% |
| 8 | Andrews, D. W., Cheng, X., Guggenberger, P (2020) Generic results for establishing the asymptotic size of confidence sets and tests | 0.817 | 11 | 5 | 55% |
| 9 | Crump, R. K., Hotz, V. J., Imbens, G. W., Mitnik, O. A (2008) Nonparametric tests for treatment effect heterogeneity | 0.811 | 4 | 2 | 100% |
| 10 | Manski, C. F (2004) Statistical treatment rules for heterogeneous populations | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 34 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating Treatment Effects Under Bounded Heterogeneity | 0.737 | 3 | 2 |
| 2 | Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions | 0.405 | 1 | 1 |