摘要
倾向值方法(加权或者匹配)在社会科学量化研究中得到越来越多的应用,但是经由倾向值方法处理的样本并不必然能够达成理想的混淆因素平衡性。混淆因素的不平衡性问题可以从理论与操作层面进行辨析。从理论上讲,传统倾向值方法依据的是等比例误差削减分析框架。这个框架虽然有其吸引力,但背后有一系列难以满足的假设条件。正因如此,倾向值方法有时无法很好地平衡混淆因素。与之相比,一个更加适配社会科学经验研究的倾向值分析框架是单调性不平衡划界框架。在操作层面上,与单调性不平衡划界分析框架一致,有三种新兴的分析方法(粗粒度精确匹配、熵平衡法与混淆因素平衡倾向值法)可以确保混淆因素在实验组与控制组之间的平衡。
The propensity score method(whether through weighting or matching)is increasingly applied in quantitative research in the social sciences.However,samples processed using the propensity score method do not necessarily achieve ideal covariate balance.The problem of covariate imbalance can be analyzed from both theoretical and practical perspectives.Theoretically,traditional propensity score methods are based on the proportional reduction of error framework.While this framework has its appeal,it relies on a series of assumptions that are often difficult to meet.Consequently,propensity score methods sometimes fail to adequately balance covariates.In contrast,a more suitable framework for propensity score analysis in social science research is the monotonic imbalance bounding framework.On the practical side,consistent with the monotonic imbalance bounding framework,three emerging analytical methods-coarse exact matching,entropy balancing,and covariate-balancing propensity scores-can ensure covariate balance between treatment and control groups.The methodological advantages of these approaches are demonstrated through two empirical examples.
作者
胡安宁
袁野
Hu Anning;Yuan Ye(Department of Sociology,Fudan University,Shanghai 200433)
出处
《浙江社会科学》
北大核心
2025年第6期58-71,85,158,共16页
Zhejiang Social Sciences
关键词
倾向值
等比例误差削减
单调性不平衡划界
粗粒度精确匹配
熵平衡
法
混淆因素平衡倾向值法
propensity score
equal proportional bias reduction
monotonic imbalance bounding
coarsened exact matching
entropy balancing
covariate balance propensity scores
