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'''Principal stratification''' is a [[statistical]] technique used in [[causal inference]] when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE).

'''Principal stratification''' is a [[statistical]] technique used in [[causal inference]] when adjusting results for post-treatment covariates. The idea is to identify underlying strata and then compute causal effects only within strata. It is a generalization of the local average treatment effect (LATE).

主分层是一种应用于因果推断的统计技术，它根据处置后协变量来调整因果效应。其基本思想是识别潜在的分层结构，然后只计算每一层的因果效应'''。'''这就是所谓的局部平均处理效应(LATE)。

主分层是一种应用于因果推断的统计技术，它根据处置后协变量来调整因果效应。其基本思想是识别潜在的分层结构，然后只计算每一层的因果效应'''。'''这就是所谓的局部平均处理效应(LATE)。

==Example==

==Example示例==

 

An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:

An example of principal stratification is where there is attrition in a randomized controlled trial. With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be:

# those who always stay in the study regardless of which treatment they were assigned

# those who always stay in the study regardless of which treatment they were assigned

如果研究人员知道每个受试者属于哪种情形，那么研究人员只需比较第一种情况下的结果，并估计出对该群提有效的因果效应。然而，研究人员并不知道这些信息，因此这种方法需要模型假设。

如果研究人员知道每个受试者属于哪种情形，那么研究人员只需比较第一种情况下的结果，并估计出对该群提有效的因果效应。然而，研究人员并不知道这些信息，因此这种方法需要模型假设。

Using the principal stratification framework also permits providing bounds for the estimated effect (under different bounding assumptions), which is common in situations with attrition.  

Using the principal stratification framework also permits providing bounds for the estimated effect (under different bounding assumptions), which is common in situations with attrition.  

使用主分层框架还允许为估计效应提供界限(在不同的界限假设下) ，这在退出偏移的情况下很常见。

使用主分层框架还允许为估计效应提供界限(在不同的界限假设下) ，这在退出偏移的情况下很常见。

In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.

In applied evaluation research, principal strata are commonly referred to as "endogenous" strata or "subgroups" and involve specialized methods of analysis for examining the effects of interventions or treatments in the medical and social sciences.

在评价研究应用中，主成分层通常被称为”内生”层或”亚群体”，并涉及专门的分析方法，用来检查医学和社会科学中的干预或处置的效果。

在评价研究应用中，主成分层通常被称为”内生”层或”亚群体”，并涉及专门的分析方法，用来检查医学和社会科学中的干预或处置的效果。

==See also==

==See also进一步可看==

*[[Instrumental variable]]

*[[Instrumental variable]]

*[[Rubin causal model]]

*[[Rubin causal model]]

==References参考==

 

= = = = = = = = 工具变量 · 虚拟事实模型

 

==References==

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