Causal inference with general treatment regimes: Generalizing the propensity score
Causal inference with general treatment regimes: Generalizing the propensity score
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DOI：
10.1198/016214504000001187
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发表时间：
2004-09-01
影响因子：
3.7
通讯作者：
van Dyk, DA
中科院分区：
文献类型：
--
作者：
Imai, K;van Dyk, DA
在本文中，我们发展了倾向函数的理论性质，它是Rosenbaum和Rubin倾向得分的推广。长期以来，基于倾向评分的方法一直被用于观察性研究中的因果推理；它们易于使用，可以有效地减少非随机处理分配引起的偏差。虽然治疗方案在实践中不一定是二元的，但倾向评分方法通常局限于二元治疗方案。对于顺序处理和分类处理，提出了两种可能的例外。在这篇文章中，我们发展的理论和方法，包括所有这些技术，并扩大其适用性，允许任意治疗制度。我们通过将倾向函数方法应用于两个数据集来说明我们的倾向函数方法；我们估计了吸烟对医疗支出的影响和受教育对工资的影响。我们还进行了模拟研究，以调查我们的方法的性能。
In this article we develop the theoretical properties of the propensity function, which is a generalization of the propensity score of Rosenbaum and Rubin. Methods based on the propensity score have long been used for causal inference in observational studies; they are easy to use and can effectively reduce the bias caused by nonrandom treatment assignment. Although treatment regimes need not be binary in practice, the propensity score methods are generally confined to binary treatment scenarios. Two possible exceptions have been suggested for ordinal and categorical treatments. In this article we develop theory and methods that encompass all of these techniques and widen their applicability by allowing for arbitrary treatment regimes. We illustrate our propensity function methods by applying them to two datasets; we estimate the effect of smoking on medical expenditure and the effect of schooling on wages. We also conduct simulation studies to investigate the performance of our methods.