Sung Jae Jun, Federico Zincenko
arXiv 20 Jun 2026 · Econometrics
arXiv:2606.22255 · PDF · DOI · OpenAlex · Extracted main text
We model unobserved confounding through an unknown finite number of latent types. This assumption induces finite-mixture representations of the treated and control outcome distributions. Using the identified mixture components, we characterize the sharp identified set for the number of latent types and derive the sharp identified set for the average treatment effect (ATE) corresponding to each admissible value, thereby providing a natural framework for sensitivity analysis. We further obtain a cutoff beyond which the identified set for the ATE coincides with a version of the Manski bounds, whereas below the cutoff it is strictly smaller. This cutoff grows only linearly with the numbers of mixture components in the treated and control groups, although the maximum admissible number of latent types grows quadratically. We also provide estimation and inference procedures with asymptotic guarantees and illustrate our methodology using LaLonde's data.
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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 | Imbens, Guido W (2003) Sensitivity to exogeneity assumptions in program evaluation | 1.000 | 13 | 3 | 100% |
| 2 | Gardner, John (2020) Identification and estimation of average causal effects when treatment status is ignorable within unobserved strata | 1.000 | 7 | 3 | 100% |
| 3 | Chen, J. and Khalili, A (2009) Order selection in finite mixture models with a nonsmooth penalty | 0.941 | 12 | 5 | 83% |
| 4 | LaLonde, Robert J (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.928 | 4 | 3 | 100% |
| 5 | Masten, Matthew A and Poirier, Alexandre (2018) Identification of treatment effects under conditional partial independence | 0.874 | 5 | 2 | 100% |
| 6 | Yakowitz, S. J. and Spragins, J. D (1968) On the identifiability of finite mixtures | 0.843 | 4 | 3 | 75% |
| 7 | McLachlan, G. J. and D. Peel (2000) Finite Mixture Models | 0.843 | 3 | 3 | 100% |
| 8 | Manski, Charles F (2003) Partial identification of probability distributions | 0.644 | 2 | 2 | 100% |
| 9 | Manski, Charles F (2010) Partial identification in econometrics | 0.644 | 2 | 2 | 100% |
| 10 | Bonvini, Matteo and Kennedy, Edward H (2022) Sensitivity analysis via the proportion of unmeasured confounding | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 45 scored citations.