Statistical Causal Inferences and Their Applications in Public Health Research
Statistical Causal Inferences and Their Applications in Public Health Research
复制标题
统计因果推断及其在公共卫生研究中的应用
DOI：
10.1007/978-3-319-41259-7_5
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Fu B
中科院分区：
文献类型：
--
作者：
Fu B
倾向评分方法（包括加权、匹配或分层）已越来越多地用于控制观察性研究和非随机试验中的潜在混杂因素，以获得治疗或干预的因果效应。然而，有一些研究，以解决缺失的混杂数据问题的倾向得分估计，这是唯一的，不同于大多数缺失的协变量数据的问题，其目标是参数估计。我们将回顾现有的方法来解决缺失的混淆数据的倾向评分方法的因果推理和讨论之间的差距差距目前的方法学发展在这一领域和分析真实的观测数据的挑战。
Propensity score methods, including weighting, matching, or stratification, have been increasingly used to control potential confounding in observational studies and non-randomized trials to obtain causal effects of treatment or intervention. However, there are few studies to address the missing confounder data problem in propensity score estimation which is unique and different from most missing covariate data problems where the goal is parameter estimation. We will review existing methods for addressing missing confounder data in propensity score methods for causal inference and discuss the gap between current methodology developments in this area and the challenges in analyzing real observational data.