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.

翻译：因果推断方法可应用于利用观察性研究数据，估计点暴露或处理对感兴趣结果的影响。例如，在女性机构间HIV研究中，理解监禁对监禁后性伴侣数量及吸烟数量的影响具有重要意义。在此类结果表现为计数的情况下，估计目标通常是因果均值比，即暴露下的反事实平均计数与未暴露下的反事实平均计数之比。本文考虑了基于逆概率处理权重、参数化g公式及双重稳健估计的因果均值比估计量，这些方法均能解释测量结果中的过度离散、零膨胀及堆积现象。通过模拟方法对这些估计量进行比较，并将其应用于女性机构间HIV研究的数据。