Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity

2026-06-08

AI Summary

This study addresses partial identification of the complier intensive-margin treatment effect in the presence of endogenous treatment assignment and nonrandom sample selection. By introducing a weak sample selection monotonicity assumption, the paper derives sharper bounds than those in Chen and Flores (2015). It further combines semiparametric orthogonal moment conditions with debiased machine learning to achieve root-n consistent and asymptotically efficient inference, even with high-dimensional covariates or flexible functional forms. Simulation evidence demonstrates favorable finite-sample performance, and empirical applications to the Job Corps and Oregon Health Insurance Experiment substantially tighten both the identified bounds and confidence intervals for the treatment effect.

Abstract

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.

Problem

• sample selection
• treatment endogeneity
• partial identification
• noncompliance
• treatment effects

Innovation

• partial identification
• sharp bounds
• endogenous treatment
• orthogonal moments
• debiased machine learning