arXiv 6 Nov 2024 · Econometrics
arXiv:2411.04312 · PDF · DOI · OpenAlex · Extracted main text
We study causal inference in sample selection models where a continuous or multivalued treatment affects both outcomes and their observability (e.g., employment or survey responses). We generalized the widely used Lee (2009)'s bounds for binary treatment effects. Our key innovation is a sufficient treatment values assumption that imposes weak restrictions on selection heterogeneity and is implicit in separable threshold-crossing models, including monotone effects on selection. Our double debiased machine learning estimator enables nonparametric and high-dimensional methods, using covariates to tighten the bounds and capture heterogeneity. Applications to Job Corps and CCC program evaluations reinforce prior findings under weaker assumptions.
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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., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 6 | 3 | 100% |
| 2 | Aizer, A., N. Early, S. Eli, G. Imbens, K. Lee, A. Lleras-Muney, and… (2024) The lifetime impacts of the new deal's youth employment program | 0.950 | 7 | 3 | 86% |
| 3 | Lee, D (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 0.944 | 19 | 5 | 84% |
| 4 | Colangelo, K. and Y.-Y. Lee (2025) Double debiased machine learning nonparametric inference with continuous treatments | 0.933 | 16 | 5 | 81% |
| 5 | Flores, C. A., A. Flores-Lagunes, A. Gonzalez, and T. C. Neumann (2012) Estimating the effects of length of exposure to instruction in a training program: The case of Job Corps | 0.928 | 4 | 3 | 100% |
| 6 | Semenova, V (2024) Generalized Lee bounds | 0.874 | 5 | 2 | 100% |
| 7 | Chen, J. and J. Roth (2023) Logs with zeros? some problems and solutions | 0.843 | 3 | 3 | 100% |
| 8 | Vytlacil, E (2002) Independence, monotonicity, and latent index models: An equivalence result | 0.811 | 4 | 2 | 100% |
| 9 | Hsu, Y.-C., M. Huber, Y.-Y. Lee, and C.-A. Liu (2023) Testing monotonicity of mean potential outcomes in a continuous treatment with high-dimensional data | 0.737 | 3 | 3 | 67% |
| 10 | DiNardo, J., J. Matsudaira, J. McCrary, and L. Sanbonmatsu (2021) A practical proactive proposal for dealing with attrition: Alternative approaches and an empirical example | 0.737 | 3 | 2 | 100% |
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