arXiv 8 Jun 2026 · Econometrics
arXiv:2606.09223 · PDF · DOI · OpenAlex · Extracted main text
This paper provides partial identification and inference for treatment effects in nonparametric sample selection models with endogenous treatment and (weak) sample selection monotonicity. Outcomes are observed only for a non-randomly selected subsample and treatment is endogenous because of noncompliance with assignment. The proposed bounds for intensive margin treatment effects among compliers are sharp and tighter than those of Chen and Flores (2015). For inference, we develop semiparametrically efficient orthogonal moments and a debiased machine learning procedure that permits valid root-$n$ inference under high-dimensional covariates and/or flexible functional forms. Simulation results indicate good finite sample performance. Applications to Job Corps and the Oregon Health Insurance Experiment show that the method can deliver substantially tighter effect bounds and confidence intervals than existing alternatives.
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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 | Heiler, Phillip (2024) Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Pol… self | 0.941 | 6 | 4 | 83% |
| 2 | Huber, Martin and Mellace, Giovanni (2015) Sharp bounds on causal effects under sample selection | 0.928 | 4 | 3 | 100% |
| 3 | Phillip Heiler and Asbjørn Kaufmann and Bezirgen Veliyev (2024) Treatment Evaluation at the Intensive and Extensive Margins self | 0.916 | 13 | 6 | 77% |
| 4 | Vira Semenova (2025) Generalized Lee bounds | 0.909 | 8 | 6 | 75% |
| 5 | Lee, David S (2009) Training, wages, and sample selection: Estimating sharp bounds on treatment effects | 0.883 | 16 | 5 | 69% |
| 6 | Chen, Xuan and Flores, Carlos A (2015) Bounds on treatment effects in the presence of sample selection and noncompliance: the wage effects of job corps | 0.874 | 12 | 6 | 67% |
| 7 | Stoye, Jörg (2020) A Simple, Short, but Never-Empty Confidence Interval for Partially Identified Parameters | 0.874 | 6 | 4 | 67% |
| 8 | Frölich, Markus (2007) Nonparametric IV Estimation of Local Average Treatment Effects with Covariates | 0.843 | 4 | 4 | 75% |
| 9 | Zhang, Junni L and Rubin, Donald B (2003) Estimation of causal effects via principal stratification when some outcomes are truncated by “death” | 0.811 | 4 | 2 | 100% |
| 10 | Chernozhukov, Victor and Lee, Sokbae and Rosen, Adam M (2013) Intersection Bounds: Estimation and Inference | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Conformalized Lee Inference: Distribution-Free Individual Treatment Effect Intervals under Monotone Sample Selection | 0.511 | 2 | 1 |