[论文解读] Propensity Score Weighting for Causal Inference with Multiple Treatments

Fan Li, Li, Fan|arXiv (Cornell University)|Aug 16, 2018

Advanced Causal Inference Techniques参考文献 58被引用 9

一句话总结

本文提出了一种用于多重处理因果推断的统一平衡权重框架，引入广义重叠权重以最小化渐近方差，并通过聚焦于协变量重叠最多的目标人群来避免极端倾向得分。该方法在观察性研究中提升了估计效率与稳健性，尤其适用于卫生服务研究中的种族差异分析。

ABSTRACT

Causal or unconfounded descriptive comparisons between multiple groups are common in observational studies. Motivated from a racial disparity study in health services research, we propose a unified propensity score weighting framework, the balancing weights, for estimating causal effects with multiple treatments. These weights incorporate the generalized propensity scores to balance the weighted covariate distribution of each treatment group, all weighted toward a common pre-specified target population. The class of balancing weights include several existing approaches such as the inverse probability weights and trimming weights as special cases. Within this framework, we propose a set of target estimands based on linear contrasts. We further develop the generalized overlap weights, constructed as the product of the inverse probability weights and the harmonic mean of the generalized propensity scores. The generalized overlap weighting scheme corresponds to the target population with the most overlap in covariates across the multiple treatments. These weights are bounded and thus bypass the problem of extreme propensities. We show that the generalized overlap weights minimize the total asymptotic variance of the moment weighting estimators for the pairwise contrasts within the class of balancing weights. We consider two balance check criteria and propose a new sandwich variance estimator for estimating the causal effects with generalized overlap weights. We apply these methods to study the racial disparities in medical expenditure between several racial groups using the 2009 Medical Expenditure Panel Survey (MEPS) data. Simulations were carried out to compare with existing methods.

研究动机与目标

• 为解决多重处理设置下逆概率加权方法的局限性，特别是极端倾向得分和目标人群不明确的问题。
• 开发一种统一的平衡权重框架，通过针对预设人群实现多重处理的因果推断。
• 提出广义重叠权重作为有界且高效的加权方案，强调在所有处理组之间具有显著重叠的子群体。
• 提供方差估计量和平衡检验，以支持广义重叠加权方案下的有效推断。
• 使用2009年医疗支出调查（MEPS）中的真实世界数据，对种族差异进行应用与验证。

提出的方法

• 提出一种广义平衡权重框架，通过重新加权协变量分布，使多重处理组在共同目标人群中实现平衡。
• 使用潜在结果的线性对比定义目标 estimands，支持处理效应的成对比较。
• 提出广义重叠权重作为逆概率权重与广义倾向得分调和平均的乘积，确保权重严格介于0和1之间。
• 推导广义重叠权重，使其在平衡权重类中最小化成对对比的总渐近方差。
• 开发一种新的sandwich方差估计量，以考虑广义重叠权重估计中的不确定性。
• 应用秩替换调整方法以处理非正则估计量，并在小样本中提升稳健性。

实验结果

研究问题

• RQ1 如何将倾向得分加权推广到多重处理场景，同时确保平衡性并最小化方差？
• RQ2 在多重处理设置下，因果推断的最优目标人群是什么？如何对其进行形式化定义？
• RQ3 能否构建有界的权重，以避免在多重处理场景中由极端倾向得分引起的不稳定性？
• RQ4 广义重叠加权方案与现有方法相比，在效率和稳健性方面表现如何？
• RQ5 模型误设对所提出加权估计量性能的影响是什么？

主要发现

• 广义重叠权重被证明在平衡权重类中最小化了成对对比的总渐近方差。
• 广义重叠权重严格介于0和1之间，有效消除了逆概率加权中因极端倾向得分导致的偏差问题。
• 所提出的sandwich方差估计量即使在模型误设情况下，也能为广义重叠权重提供有效的推断。
• 模拟结果表明，广义重叠权重在偏差和均方误差方面优于现有方法，尤其在重叠较差时表现更优。
• 在MEPS应用中，该方法揭示了医疗支出中的显著种族差异，广义重叠权重提供了比标准IPW更稳定、更具解释性的估计结果。
• 该方法支持增广加权估计量的使用，当结果模型和倾向得分模型均正确指定时，可进一步提升效率。