Propensity Score Matching Methods 倾向得分匹配方法
Conclusion For large and complicated data, propensity score methods had more flexibility in p...
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。
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。
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