论文标题
通过观察数据对计数结果的暴露影响，并应用于被监禁的妇女
Exposure Effects on Count Outcomes with Observational Data, with Application to Incarcerated Women
论文作者
论文摘要
可以利用观察性研究的数据应用因果推理方法来估计点暴露或治疗对感兴趣结果的影响。例如，在妇女的机构艾滋病毒研究中，了解监禁对性伴侣数量的影响以及被监禁后吸烟的数量。在这样的设置中，结果是计数，估计数通常是因果平均比率，即，在没有暴露的情况下，在暴露于反事实平均值的情况下，反事实平均计数的比率。本文考虑了基于治疗权重，参数G形式和双重稳健估计的因果均值比的估计值，每个估计值都可以解释过度分散，零发出和测量结果中的堆积。在模拟中比较方法，并将其应用于妇女间艾滋病毒研究中的数据。
Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the Women's Interagency HIV Study, it is of interest to understand the effects of incarceration on the number of sexual partners and the number of cigarettes smoked after incarceration. In settings like this where the outcome is a count, the estimand is often the causal mean ratio, i.e., the ratio of the counterfactual mean count under exposure to the counterfactual mean count under no exposure. This paper considers estimators of the causal mean ratio based on inverse probability of treatment weights, the parametric g-formula, and doubly robust estimation, each of which can account for overdispersion, zero-inflation, and heaping in the measured outcome. Methods are compared in simulations and are applied to data from the Women's Interagency HIV Study.
