Anatomy of the Selection Problem
重新审视了因变量y被选择性观测时估计回归E(y|x)的问题，提出两种识别回归的先验限制：一种是新的对y取值范围的边界限制，另一种是来自潜变量模型的可分离性限制。
This article considers anew the problem of estimating a regression E(y|x) when realizations of (y, x) are sampled randomly but y is observed selectively. The central issue is the failure of the sampling process to identify E(y|x). The problem faced by the researcher is to find correct prior restrictions which, when combined with the data, identify the regression. Two kinds of restrictions are examined here. One, which has not been studied before, is a bound on the support of y. Such a bound implies a simple, useful bound on E(y|x). The other, which has received much attention, is a separability restriction derived from a latent variable model.