Assessing the causal effects of a stochastic intervention in time series data: are heat alerts effective in preventing deaths and hospitalizations?

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Xiao Wu, Kate R Weinberger, Gregory A Wellenius, Francesca Dominici, Danielle Braun
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Abstract

The methodological development of this article is motivated by the need to address the following scientific question: does the issuance of heat alerts prevent adverse health effects? Our goal is to address this question within a causal inference framework in the context of time series data. A key challenge is that causal inference methods require the overlap assumption to hold: each unit (i.e., a day) must have a positive probability of receiving the treatment (i.e., issuing a heat alert on that day). In our motivating example, the overlap assumption is often violated: the probability of issuing a heat alert on a cooler day is near zero. To overcome this challenge, we propose a stochastic intervention for time series data which is implemented via an incremental time-varying propensity score (ItvPS). The ItvPS intervention is executed by multiplying the probability of issuing a heat alert on day $t$-conditional on past information up to day $t$-by an odds ratio $\delta_t$. First, we introduce a new class of causal estimands, which relies on the ItvPS intervention. We provide theoretical results to show that these causal estimands can be identified and estimated under a weaker version of the overlap assumption. Second, we propose nonparametric estimators based on the ItvPS and derive an upper bound for the variances of these estimators. Third, we extend this framework to multisite time series using a spatial meta-analysis approach. Fourth, we show that the proposed estimators perform well in terms of bias and root mean squared error via simulations. Finally, we apply our proposed approach to estimate the causal effects of increasing the probability of issuing heat alerts on each warm-season day in reducing deaths and hospitalizations among Medicare enrollees in 2837 US counties.

评估时间序列数据中随机干预措施的因果效应:高温预警是否能有效预防死亡和住院?
本文在方法论方面的发展是出于解决以下科学问题的需要:发布高温预警是否能预防对健康的不利影响?我们的目标是在时间序列数据的因果推断框架内解决这个问题。一个关键的挑战是,因果推断方法要求重叠假设成立:每个单位(即一天)必须有接受治疗(即在这一天发布高温预警)的正概率。在我们的激励示例中,重叠假设经常被违反:在较凉爽的一天发布高温预警的概率接近于零。为了克服这一难题,我们提出了一种针对时间序列数据的随机干预方法,通过增量时变倾向得分(ItvPS)来实现。ItvPS 干预的执行方式是将在 $t$ 日发布高温预警的概率--以截至 $t$ 日的过去信息为条件--乘以一个几率比 $\delta_t$。首先,我们引入了一类新的因果关系估计值,它依赖于 ItvPS 干预。我们提供的理论结果表明,这些因果估计值可以在较弱版本的重叠假设下进行识别和估计。其次,我们提出了基于 ItvPS 的非参数估计器,并推导出了这些估计器的方差上限。第三,我们利用空间元分析方法将这一框架扩展到多站点时间序列。第四,我们通过模拟表明,所提出的估计器在偏差和均方根误差方面表现良好。最后,我们应用我们提出的方法来估计在每个暖季日提高发布高温预警的概率对减少美国 2837 个县的医疗保险参保者死亡和住院人数的因果效应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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