缺乏正向性的因果推理:重叠权重的作用

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Roland A. Matsouaka, Yunji Zhou
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引用次数: 0

摘要

当违反正向性假设时,如何分析数据?文献中存在几种可能的解决方案。在本文中,我们考虑了倾向得分(PS)方法,这些方法通常用于观察性研究,以评估违反正向性假设情况下的因果治疗效果。我们关注并研究了反概率加权(IPW)修剪和截断的四种具体替代方案:匹配权重(MW)、香农熵权重(EW)、重叠权重(OW)和贝塔权重(BW)估计器。我们首先明确其目标人群,即临床等效的患者人群,也就是我们有足够 PS 重叠的人群。然后,我们在不同的相应权重(和估计器)之间建立联系;这样我们就能突出这些估计器的共同特性和理论意义。最后,我们介绍了它们的增强估计器,这些估计器利用了倾向得分和结果回归模型的估计优势,在偏差和效率方面增强了治疗效果估计器。我们还阐明了 OW 估计器的作用,它是所有这些方法中针对重叠人群的旗舰方法。我们的分析结果表明,当存在中度或极端(随机或结构性)违反正向性假设的情况时,OW、MW 和 EW 比 IPW 和某些情况下的 BW 更优。然后,我们通过蒙特卡罗模拟对上述估计器的有限样本性能进行评估、比较和确认。最后,我们使用两个以违反正向性假设为特征的实际数据示例来说明这些方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Causal inference in the absence of positivity: The role of overlap weights

How to analyze data when there is violation of the positivity assumption? Several possible solutions exist in the literature. In this paper, we consider propensity score (PS) methods that are commonly used in observational studies to assess causal treatment effects in the context where the positivity assumption is violated. We focus on and examine four specific alternative solutions to the inverse probability weighting (IPW) trimming and truncation: matching weight (MW), Shannon's entropy weight (EW), overlap weight (OW), and beta weight (BW) estimators.

We first specify their target population, the population of patients for whom clinical equipoise, that is, where we have sufficient PS overlap. Then, we establish the nexus among the different corresponding weights (and estimators); this allows us to highlight the shared properties and theoretical implications of these estimators. Finally, we introduce their augmented estimators that take advantage of estimating both the propensity score and outcome regression models to enhance the treatment effect estimators in terms of bias and efficiency. We also elucidate the role of the OW estimator as the flagship of all these methods that target the overlap population.

Our analytic results demonstrate that OW, MW, and EW are preferable to IPW and some cases of BW when there is a moderate or extreme (stochastic or structural) violation of the positivity assumption. We then evaluate, compare, and confirm the finite-sample performance of the aforementioned estimators via Monte Carlo simulations. Finally, we illustrate these methods using two real-world data examples marked by violations of the positivity assumption.

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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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