倾向评分匹配估计器有两个优点。一是它们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。如果我们非参数地估计它，我们就会遇到我们试图避免的维数诅咒问题。如果我们参数化地估计它，估计的治疗效果对倾向评分的规范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在无混淆假设下的灵敏度问题。我们发现估算对规格并不敏感。接下来，我们使用Rosenbaum和Rubin(1983)的见解，即任何低于倾向得分的得分都是平衡得分，提供了一些理论证明。然后，我们将我们的发现与Smith和Todd(2005)的发现相一致，如果无混淆性假设失败，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.