• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Psychometrika >Augmented Weighted Estimators Dealing with Practical Positivity Violation to Causal inferences in a Random Coefficient Model
【24h】

Augmented Weighted Estimators Dealing with Practical Positivity Violation to Causal inferences in a Random Coefficient Model

机译：增强加权估算，处理随机系数模型中因果推论的实际积极性违规

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The inverse probability of treatment weighted (IPTW) estimator can be used to make causal inferences under two assumptions: (1) no unobserved confounders (ignorability) and (2) positive probability of treatment and of control at every level of the confounders (positivity), but is vulnerable to bias if by chance, the proportion of the sample assigned to treatment, or proportion of control, is zero at certain levels of the confounders. We propose to deal with this sampling zero problem, also known as practical violation of the positivity assumption, in a setting where the observed confounder is cluster identity, i.e., treatment assignment is ignorable within clusters. Specifically, based on a random coefficient model assumed for the potential outcome, we augment the IPTW estimating function with the estimated potential outcomes of treatment (or of control) for clusters that have no observation of treatment (or control). If the cluster-specific potential outcomes are estimated correctly, the augmented estimating function can be shown to converge in expectation to zero and therefore yield consistent causal estimates. The proposed method can be implemented in the existing software, and it performs well in simulated data as well as with real-world data from a teacher preparation evaluation study.
机译：治疗的反概率加权（IPTW）估计器可用于在两个假设下进行因果推断：（1）没有未观察到的混淆（无知性）和（2）治疗的积极概率和控制在每个水平的混合物（阳性）但是，如果偶然，则易受偏见，分配给治疗或对照比例的​​样本的比例为零的混合物的某些水平为零。我们建议处理这种采样零问题，也称为实际违反积极假设，在观察到的混淆器是集群标识的情况下，即治疗任务在集群中是无知的。具体地，基于对潜在结果假设的随机系数模型，我们将IPTW估计功能增强了对没有观察治疗（或对照）的簇的估计潜在的治疗潜在结果（或控制）。如果正确估计了特定于簇的潜在结果，则可以显示增强估计函数来收敛到零，因此产生一致的因果估计。所提出的方法可以在现有软件中实现，并且在模拟数据以及来自教师准备评估研究的真实数据中表现良好。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号