Causal inference through potential outcomes and principal stratification: Application to studies with "censoring" due to death
Causal inference through potential outcomes and principal stratification: Application to studies with "censoring" due to death
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DOI：
10.1214/088342306000000114
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发表时间：
2006-08-01
影响因子：
5.7
通讯作者：
Rubin, Donald B.
中科院分区：
文献类型：
--
作者：
Rubin, Donald B.
因果推理最好通过潜在结果来理解。这种用法在更复杂的情况下尤其重要，即观察性研究或有并发症（如不依从性）的随机实验。这一讲的主题是评估一种治疗对被死亡“审查”的主要结果的因果效应，这是另一个这样的并发症。例如，假设我们希望在随机实验中估计一种新药对生活质量（QOL）的影响，其中一些患者在评估其生活质量的指定时间之前死亡。另一个具有相同结构的例子发生在一个旨在提高期末考试分数的教育项目的评估中，期末考试分数是为那些在考试前辍学的人定义的。进一步的应用是研究职业培训计划对工资的影响，其中工资只针对那些被雇用的人。使用潜在结果来定义因果关系，然后对中间结果（例如，生存）进行主要分层，从而极大地阐明了对此类示例的分析。
Causal inference is best understood using potential outcomes. This use is particularly important in more complex settings, that is, observational studies or randomized experiments with complications such as noncompliance. The topic of this lecture, the issue of estimating the causal effect of a treatment on a primary outcome that is "censored" by death, is another such complication. For example, suppose that we wish to estimate the effect of a new drug on Quality of Life (QOL) in a randomized experiment, where some of the patients die before the time designated for their QOL to be assessed. Another example with the same structure occurs with the evaluation of an educational program designed to increase final test scores, which are riot defined for those who drop out of school before taking the test. A further application is to studies of the effect of job-training programs on wages, where wages are only defined for those who are employed. The analysis of examples like these is greatly clarified using potential outcomes to define causal effects, followed by principal stratification on the intermediated outcomes (e.g., survival).