Propensity score weighting is an important tool for comparative effectiveness research.Besides the inverse probability of treatment weights (IPW), recent development has introduced a general class of balancing weights, corresponding to alternative target populations and estimands. In particular, the overlap weights (OW) lead to optimal covariate balance and estimation efficiency, and a target population of scientific and policy interest. We develop the R package PSweight to provide a comprehensive design and analysis platform for causal inference based on propensity score weighting. PSweight supports (i) a variety of balancing weights, (ii) binary and multiple treatments,(iii) simple and augmented weighting estimators, (iv) nuisance-adjusted sandwich variances, and(v) ratio estimands. PSweight also provides diagnostic tables and graphs for covariate balance assessment. We demonstrate the functionality of the package using a data example from the NationalChild Development Survey (NCDS), where we evaluate the causal effect of educational attainment on income.
翻译:除了治疗权重的反概率外,最近的发展还引入了一个平衡权重的一般类别,与替代目标人群和估计量相对应,特别是,重叠权重(OW)导致最佳的共变平衡和估计效率,以及科学和政策利益对象群。我们开发了R包PS重量,以根据偏差权重来提供一个全面设计和分析因果推断的平台。PS重量支持(一)各种平衡权重,(二)二)二和多项治疗,(三)简单和强化的加权估量器,(四)微调调整型三明治差异,和(五)比率估计值。PS重量还提供诊断表和图表,用于对共变平衡进行评估。我们用国家儿童发展调查(NCDS)提供的数据示例来显示该包的功能,我们在此评估教育成果对收入的因果关系。