Causal Quantile Treatment Effects with missing data by double-sampling
通过双重抽样处理缺失数据的因果分位数处理效应AI 解读 ↓
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通过双重抽样处理缺失数据的因果分位数处理效应
- 研究问题
- 当结局可能非随机缺失时，如何借助对部分缺失个体的补充抽样，识别和估计因果加权分位数处理效应并进行统一推断？
- 方法与思路
- 作者利用双重抽样补充原本缺失的结局，提出逆概率加权估计量，推导逐点与分位数区间上的渐近性质，并设计自助推断。
- 材料陈述的贡献
- 摘要称，补充数据可支持不约束原始缺失机制的识别条件，且允许估计倾向得分和补充抽样概率；减重手术数据用于展示应用。
- 阅读关注点
- 应核对双重抽样与因果识别的具体条件、权重构造、分位数区间限制，以及估计抽样概率时统一推断和自助法的有效性。
- 阅读边界
- 这是预印本，不能据此声称已同行评审或期刊接收。不要求原始缺失机制假设仍需其他识别条件；摘要未说明其具体内容和数值结果。
原文摘要
Causal weighted quantile treatment effects (WQTE) are a useful complement to standard causal contrasts that focus on the mean when interest lies at the tails of the counterfactual distribution. To-date, however, methods for estimation and inference regarding causal WQTEs have assumed complete data on all relevant factors. In most practical settings, however, data will be missing or incomplete data, particularly when the data are not collected for research purposes, as is the case for electronic health records and disease registries. Furthermore, such data sources may be particularly susceptible to the outcome data being missing-not-at-random (MNAR). In this paper, we consider the use of double-sampling, through which the otherwise missing data are ascertained on a sub-sample of study units, as a strategy to mitigate bias due to MNAR data in the estimation of causal WQTEs. With the additional data in-hand, we present identifying conditions that do not require assumptions regarding missingness in the original data. We then propose a novel inverse-probability weighted estimator and derive its asymptotic properties, both pointwise at specific quantiles and uniformly across a range of quantiles over some compact subset of (0,1), allowing the propensity score and double-sampling probabilities to be estimated. For practical inference, we develop a bootstrap method that can be used for both pointwise and uniform inference. A simulation study is conducted to examine the finite sample performance of the proposed estimators. The proposed method is illustrated with data from an EHR-based study examining the relative effects of two bariatric surgery procedures on BMI loss at 3 years post-surgery.