Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
利用多个替代指标估计处理效应：替代得分与替代指数AI 解读 ↓
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利用多个替代指标估计处理效应：替代得分与替代指数
- 研究问题
- 当实验期内无法观察长期结局时，如何结合多个中间指标与另一份观察数据识别长期平均处理效应，并评估替代条件失效的偏差？
- 方法与思路
- 作者结合记录处理和替代指标的实验样本，以及记录替代指标和长期结局的观察样本，研究高维指标共同满足替代条件时的识别估计。
- 材料陈述的贡献
- 摘要称，论文给出多指标下平均处理效应的识别与估计条件，推导替代条件违反时的偏差，并说明实验已观察结局时指标仍可提供信息。
- 阅读关注点
- 应核对联合替代条件、两样本衔接条件及替代得分和替代指数的具体作用，并检查偏差表达式及额外信息如何改善估计。
- 阅读边界
- 这是 arXiv 预印本，未核验同行评审或期刊接收；摘要未列出全部识别条件，拥有大量指标本身不能证明它们共同满足替代条件。
原文摘要
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.