arXiv 2 Mar 2023 · Econometrics
arXiv:2303.00982 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a novel framework of aggregated intersection of regression functions, where the target parameter is obtained by averaging the minimum (or maximum) of a collection of regression functions over the covariate space. Such quantities include the lower and upper bounds on distributional effects (Frechet-Hoeffding, Makarov) and the optimal welfare in the statistical treatment choice problem. The proposed estimator -- the envelope score estimator -- is shown to have an oracle property, where the oracle knows the identity of the minimizer for each covariate value. I apply this result to the bounds in the Roy model and the Horowitz-Manski-Lee bounds with a discrete outcome. The proposed approach performs well empirically on the data from the Oregon Health Insurance Experiment.
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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 | Kitagawa, T. and Tetenov, A (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 1.000 | 5 | 3 | 100% |
| 2 | Mourifié, I., Henry, M., and Meango, R (2020) Sharp bounds and testability of a roy model of stem major choices | 0.928 | 5 | 3 | 80% |
| 3 | Qian, M. and Murphy, S. A (2011) Performance guarantees for individualized treatment rules | 0.928 | 4 | 4 | 100% |
| 4 | Semenova, V (2020) Generalized lee bounds self | 0.928 | 4 | 3 | 100% |
| 5 | Finkelstein, A., Taubman, S., Wright, B., Bernstein, M., Gruber, J.,… (2012) The oregon health insurance experiment: Evidence from the first year | 0.928 | 4 | 3 | 100% |
| 6 | Luedtke, A. and van der Laan, M (2016) Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy | 0.920 | 9 | 5 | 78% |
| 7 | Fan, Y. and Park, S. S (2010) Sharp bounds on the distribution of treatment effects and their statistical inference | 0.874 | 7 | 2 | 100% |
| 8 | Semenova, V (2023) Debiased machine learning for set-identified linear models self | 0.843 | 4 | 3 | 75% |
| 9 | Chandrasekhar, A., Chernozhukov, V., Molinari, F., and Schrimpf, P (2012) Inference for best linear approximations to set identified functions | 0.811 | 4 | 2 | 100% |
| 10 | Lee, D (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 98 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Training and Testing with Multiple Splits: A Central Limit Theorem for Split-Sample Estimators | 0.405 | 1 | 1 |
| 2 | Partial Identification under Stratified Randomization | 0.405 | 1 | 1 |