The literature on stochastic programming typically restricts attention to problems that fulfill constraint qualifications. The literature on estimation and inference under partial identification frequently restricts the geometry of identified sets with diverse high-level assumptions. These superficially appear to be different approaches to closely related problems. We extensively analyze their relation. Among other things, we show that for partial identification through pure moment inequalities, numerous assumptions from the literature essentially coincide with the Mangasarian–Fromowitz constraint qualification. This clarifies the relation between well-known contributions, including within econometrics, and elucidates stringency, as well as ease of verification, of some high-level assumptions in seminal papers.

关于随机规划的文献通常将注意力限制在满足约束条件的问题上。关于部分识别下的估计和推理的文献经常限制具有不同高级假设的识别集的几何形状。从表面上看，这些似乎是解决密切相关问题的不同方法。我们广泛分析它们的关系。除其他外，我们表明，对于通过纯矩不等式进行的部分识别，文献中的许多假设基本上与 Mangasarian-Fromowitz 约束条件一致。这阐明了众所周知的贡献之间的关系，包括在计量经济学中，并阐明了开创性论文中一些高级假设的严格性和易于验证。