论文标题
通过多元广义倾向评分对多个连续暴露的因果推断
Causal inference for multiple continuous exposures via the multivariate generalized propensity score
论文作者
论文摘要
广义倾向评分（GPS）是用于使用定量或连续暴露的倾向评分的扩展（例如，药物剂量或多年的教育）。当前的GPS方法允许估计单个连续暴露与结果之间的剂量反应关系。但是，在许多现实世界中，同时发生多种暴露可能与结果有关。我们提出了一种多元GPS方法（MVGP），该方法允许估计将多个连续暴露变量的联合分布与结果相关联的剂量反应表面。该方法涉及在曝光变量的多元正态性假设下产生权重。我们通过模拟专注于两个曝光变量的方案，表明MVGPS方法可以在不同的曝光变量中可能有所不同并减少各种数据生成方案中的治疗效果估计的偏差。我们将MVGPS方法应用于分析两种类型的干预策略的联合作用，以降低儿童肥胖率。
The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (e.g., dose of medication or years of education). Current GPS methods allow estimation of the dose-response relationship between a single continuous exposure and an outcome. However, in many real-world settings, there are multiple exposures occurring simultaneously that could be causally related to the outcome. We propose a multivariate GPS method (mvGPS) that allows estimation of a dose-response surface that relates the joint distribution of multiple continuous exposure variables to an outcome. The method involves generating weights under a multivariate normality assumption on the exposure variables. Focusing on scenarios with two exposure variables, we show via simulation that the mvGPS method can achieve balance across sets of confounders that may differ for different exposure variables and reduces bias of the treatment effect estimates under a variety of data generating scenarios. We apply the mvGPS method to an analysis of the joint effect of two types of intervention strategies to reduce childhood obesity rates.
