Results Under model misspecification conditions, propensity score methods had better robustness than model based methods.
结果当存在模型误定时，倾向指数方法比基于模型的方法具有较好的稳健性。
Conclusion For large and complicated data, propensity score methods had more flexibility in practical use than model based methods.
结论对于大量、关系复杂的数据，应用倾向指数方法具有较大的灵活性。
Objective to evaluate the statistical property of the estimators on exposure effect and value of propensity score methods in practical use.
目的评价由倾向指数方法得到的暴露效果的估计量和统计性质，并探讨其实用性。
We will explore new methods that combine spatial econometric techniques and methods used in the program evaluation literature such as difference-in-differences and propensity score matching methods.
我们将探索把空间经济学技术和那些被用在项目评估文学中的方法结合起来的新方法，如“异中求异”的方法和倾向匹配划分方法。
We will explore new methods that combine spatial econometric techniques and methods used in the program evaluation literature such as difference-in-differences and propensity score matching methods.
我们将探索把空间经济学技术和那些被用在项目评估文学中的方法结合起来的新方法，如“异中求异”的方法和倾向匹配划分方法。
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