Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
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发表时间：
2016-03
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通讯作者：
S. Athey;Raj Chetty;G. Imbens;Hyunseung Kang
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作者：
S. Athey;Raj Chetty;G. Imbens;Hyunseung Kang
估计治疗的长期效果在许多领域都很有意义。在估计这种治疗效果方面的一个共同挑战是，在作出政策决定所需的时间范围内无法观察到长期结果。克服这种缺失数据问题的一种方法是分析治疗对中间结果的影响，通常称为统计替代，如果它满足治疗和结果独立于统计替代的条件。代孕条件的有效性往往是有争议的。在这里，我们利用了这样一个事实，即在现代数据集中，研究人员经常观察到大量，可能是数百或数千个中间结果，这些结果被认为位于或接近治疗和长期结果之间的因果链。即使没有一个单独的代理满足统计替代标准本身，使用多个代理可以在因果推理中是有用的。我们主要关注两个样本的设置，一个实验样本包含有关治疗指标和替代品的数据，一个观察样本包含有关替代品和主要结局的信息。我们的假设下，平均治疗效果的识别和估计的代理，共同满足代孕假设的高维向量，并推导出违反代孕假设的偏差，并表明，即使主要结果也观察到在实验样本中，仍然有信息可以从使用代理人。
Estimating the long-term effects of treatments is of interest in many fields. A common challenge in estimating such treatment effects is that long-term outcomes are unobserved in the time frame needed to make policy decisions. One approach to overcome this missing data problem is to analyze treatments effects on an intermediate outcome, often called a statistical surrogate, if it satisfies the condition that treatment and outcome are independent conditional on the statistical surrogate. The validity of the surrogacy condition is often controversial. Here we exploit that fact that in modern datasets, researchers often observe a large number, possibly hundreds or thousands, of intermediate outcomes, thought to lie on or close to the causal chain between the treatment and the long-term outcome of interest. Even if none of the individual proxies satisfies the statistical surrogacy criterion by itself, using multiple proxies can be useful in causal inference. We focus primarily on a setting with two samples, an experimental sample containing data about the treatment indicator and the surrogates and an observational sample containing information about the surrogates and the primary outcome. We state assumptions under which the average treatment effect be identified and estimated with a high-dimensional vector of proxies that collectively satisfy the surrogacy assumption, and derive the bias from violations of the surrogacy assumption, and show that even if the primary outcome is also observed in the experimental sample, there is still information to be gained from using surrogates.