Many longitudinal databases record the occurrence of recurrent events over time. In this article, we propose a new method to estimate the average causal effect of a binary treatment for recurrent event data in the presence of confounders. We propose a doubly robust semiparametric estimator based on a weighted version of the Nelson-Aalen estimator and a conditional regression estimator under an assumed semiparametric multiplicative rate model for recurrent event data. We show that the proposed doubly robust estimator is consistent and asymptotically normal. In addition, a model diagnostic plot of residuals is presented to assess the adequacy of our proposed semiparametric model. We then evaluate the finite sample behavior of the proposed estimators under a number of simulation scenarios. Finally, we illustrate the proposed methodology via a database of circus artist injuries.

中文翻译：

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重复事件数据的双稳健估计和因果推断。

许多纵向数据库记录了随时间推移发生的重复事件。在本文中，我们提出了一种新方法来估计在混杂因素存在下对重复事件数据进行二进制处理的平均因果效应。我们在一个假定的半参数乘积率模型下，基于递归事件数据，基于Nelson-Aalen估计量的加权版本和条件回归估计量，提出了一个双健壮的半参数估计量。我们表明，提出的双重鲁棒估计量是一致的并且渐近正态。此外，提出了残差模型诊断图以评估我们提出的半参数模型的适当性。然后，我们在许多模拟情况下评估提议的估计量的有限样本行为。最后，