Exposure Effects on Count Outcomes with Observational Data, with Application to Incarcerated Women

TL;DR

This paper develops and compares methods for causal inference on count outcomes in observational studies, with an application to understanding incarceration effects on behaviors among women, accounting for data complexities like overdispersion.

Contribution

It introduces estimators for the causal mean ratio that handle overdispersion, zero-inflation, and heaping, and compares their performance through simulations and real data analysis.

Findings

Methods effectively account for data complexities.

Estimator comparisons highlight strengths and limitations.

Application reveals incarceration impacts on behaviors.

Abstract

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.