Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis
arXiv 14 Apr 2026 · Statistics — Methodology
arXiv:2604.12263 · PDF · DOI · OpenAlex · Extracted main text
Policy-Relevant Treatment Effects (PRTEs) are generally not point-identified under standard Instrumental Variable (IV) assumptions when the instrument generates limited support in treatment propensity. We show that PRTE partial identification in the generalized Roy model can instead be formulated as a Constrained Conditional Optimal Transport (CCOT) problem over the joint conditional law of the potential outcome and the latent resistance. The resulting multidimensional CCOT problem reduces analytically to separable one-dimensional OT problems with product costs, yielding sharp closed-form bounds and avoiding direct solution of the original high-dimensional CCOT problem. We also develop estimation and inference procedures for these bounds: for discrete instruments, we use a Double Machine Learning (DML) approach based on Neyman-orthogonal scores that accommodates high-dimensional covariates while achieving the parametric $\sqrt{n}$ rate and asymptotic normality; for continuous instruments, we explicitly characterize the corresponding nonparametric convergence rates. The framework accommodates covariates, discrete and continuous instruments, and extensions to general treatment settings. In simulations and a bed-net subsidy application, the resulting bounds are substantially tighter than the moment-relaxation method.
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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 | Magne, Mogstad and Andres, Santos and Alexander, Torgovitsky (2018) Using instrumental variables for inference about policy relevant treatment effects | 0.956 | 8 | 5 | 88% |
| 2 | Heckman, James J and Vytlacil, Edward J (1999) Local instrumental variables and latent variable models for identifying and bounding treatment effects | 0.950 | 7 | 4 | 86% |
| 3 | Heckman, James J and Vytlacil, Edward (2005) Structural equations, treatment effects, and econometric policy evaluation 1 | 0.874 | 8 | 2 | 100% |
| 4 | Han, Sukjin and Yang, Shenshen (2024) A computational approach to identification of treatment effects for policy evaluation | 0.874 | 7 | 2 | 100% |
| 5 | Marx, Philip (2024) Sharp Bounds in the Latent Index Selection Model | 0.874 | 7 | 2 | 100% |
| 6 | Heckman, James J and Vytlacil, Edward J (2001) Instrumental variables, selection models, and tight bounds on the average treatment effect | 0.843 | 3 | 3 | 100% |
| 7 | Angrist, Joshua and Imbens, Guido (1995) Identification and estimation of local average treatment effects | 0.811 | 5 | 2 | 80% |
| 8 | Heckman, James J and Vytlacil, Edward J (2007) Econometric evaluation of social programs, part II: Using the marginal treatment effect to organize alternative econometric esti… | 0.737 | 3 | 2 | 100% |
| 9 | Imbens, Guido W and Newey, Whitney K (2009) Identification and estimation of triangular simultaneous equations models without additivity | 0.737 | 3 | 2 | 100% |
| 10 | Manski, Charles F (1990) Nonparametric Bounds on Treatment Effects | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 65 scored citations.