arXiv 19 Oct 2025 · Econometrics
arXiv:2510.16681 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a nonparametric framework to identify and estimate distributional treatment effects under nonseparable endogeneity. We begin by revisiting the widely adopted rank similarity (RS) assumption and characterizing it by the relationship it imposes between observed and counterfactual potential outcome distributions. The characterization highlights the restrictiveness of RS, motivating a weaker identifying condition. Under this alternative, we construct identifying bounds on the distributional treatment effects of interest through a linear semi-infinite programming (SILP) formulation. Our identification strategy also clarifies how richer exogenous instrument variation, such as multi-valued or multiple instruments, can further tighten these bounds. Finally, exploiting the SILP's saddle-point structure and Karush-Kuhn-Tucker (KKT) conditions, we establish large-sample properties for the empirical SILP: consistency and asymptotic distribution results for the estimated bounds and associated solutions.
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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 | Milgrom, P. and I. Segal (2002) Envelope theorems for arbitrary choice sets | 1.000 | 10 | 4 | 100% |
| 2 | Bonnans, J. F. and A. Shapiro (2000) Perturbation Analysis of Optimization Problems | 0.928 | 4 | 3 | 100% |
| 3 | Imbens, G. W. and J. D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.874 | 5 | 2 | 100% |
| 4 | Pomatto, L., P. Strack, and O. Tamuz (2020) Stochastic dominance under independent noise | 0.874 | 5 | 2 | 100% |
| 5 | Chernozhukov, V. and C. Hansen (2005) An IV model of quantile treatment effects | 0.811 | 4 | 2 | 100% |
| 6 | Fang, Z. and A. Santos (2019) Inference on directionally differentiable functions | 0.811 | 4 | 2 | 100% |
| 7 | Vuong, Q. and H. Xu (2017) Counterfactual mapping and individual treatment effects in nonseparable models with binary endogeneity | 0.737 | 3 | 2 | 100% |
| 8 | Shapiro, A (1991) Asymptotic analysis of stochastic programs | 0.644 | 2 | 2 | 100% |
| 9 | Christensen, T. M. and B. Connault (2023) Counterfactual Sensitivity and Robustness | 0.511 | 2 | 1 | 100% |
| 10 | Goff, L. and E. D. Mbakop (2025) Inference on the Value of a Linear Program, Working paper | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 56 scored citations.