倾向分数匹配估计有两个优点。一是克服了协变量匹配的维数问题，二是它们是非参数的。然而，倾向得分通常是未知的，需要估计。如果我们对它进行非参数化估计，我们就会遇到我们试图避免的维数诅咒问题。如果我们对其进行参数化估计，那么估计的治疗效果对倾向得分的规格有多敏感就成为一个重要的问题。本文对这一问题进行了研究。首先，我们用蒙特卡罗实验方法研究了无混杂假设下的灵敏度问题。我们发现估价对规格并不敏感。接下来，我们使用Rosenbaum和Rubin(1983)的见解提供了一些理论依据，即任何分数小于倾向分数的分数都是平衡分数。然后，我们将我们的发现与Smith和Todd(2005)的发现相一致，即如果无混杂假设失败，匹配结果可能是敏感的。 However, failure of the unconfoundedness assumption will not necessarily result in sensitive estimates. Matching estimators can be speciously robust in the sense that the treatment effects are consistently overestimated or underestimated. Sensitivity checks applied in empirical studies are helpful in eliminating sensitive cases, but in general, it cannot help to solve the fundamental problem that the matching assumptions are inherently untestable. Last, our results suggest that including irrelevant variables in the propensity score will not bias the results, but overspecifying it (e.g., adding unnecessary nonlinear terms) probably will.