Treatment effects in sample selection models and their nonparametric estimation
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样本选择模型中的处理效应及其非参数估计
- 研究问题
- 当处理同时影响结果是否可见与潜在结果时，参与效应、可见表现效应及不可见表现效应分别在何种条件下能够识别？
- 方法与思路
- 作者分别考察选择指标、含零值的可见结果及入选者结果的组间均值差，并在两样本框架下构造非参数估计方法。
- 材料陈述的贡献
- 摘要称，论文给出三类效应的识别条件，提出允许不平衡抽样并处理支持不重叠的估计方法，还提供未观测混杂敏感性分析。
- 阅读关注点
- 应逐一核对三类效应的目标总体与识别条件，尤其是入选者均值差何时代表潜在结果效应，以及支持处理与敏感性分析。
- 阅读边界
- 仅据所给出版社摘要，不能把三个均值差视为无条件有效的因果效应；具体识别假设、支持处理和比较结果均未展开。
原文摘要
In a sample-selection model with the ‘selection’ variable Q and the ‘outcome’ variable Y∗, Y∗ is observed only when Q=1. For a treatment D affecting both Q and Y∗, three effects are of interest: ‘participation’ (i.e., the selection) effect of D on Q, ‘visible performance’ (i.e., the observed outcome) effect of D on Y≡QY∗, and ‘invisible performance’ (i.e., the latent outcome) effect of D on Y∗. This paper shows the conditions under which the three effects are identified, respectively, by the three corresponding mean differences of Q, Y, and Y|Q=1 (i.e., Y∗|Q=1) across the control (D=0) and treatment (D=1) groups. Our nonparametric estimators for those effects adopt a two-sample framework and have several advantages over the usual matching methods. First, there is no need to select the number of matched observations. Second, the asymptotic distribution is easily obtained. Third, over-sampling the control/treatment group is allowed. Fourth, there is a built-in mechanism that takes into account the ‘non-overlapping support problem’, which the usual matching deals with by choosing a ‘caliper’. Fifth, a sensitivity analysis to gauge the presence of unobserved confounders is available. A simulation study is conducted to compare the proposed methods with matching methods, and a real data illustration is provided.