Abstract Causal inference from a randomized trial becomes challenging when interest focuses on a specific subpopulation and when the causal effect involves both the randomized binary treatment and a non-randomized continuous exposure. This study is motivated by a clinical trial conducted among women of childbearing potential to evaluate the effectiveness of a malaria vaccine during pregnancy within the subpopulation of women who would have become pregnant under placebo conditions. Although the primary policy-relevant contrast concerns the binary randomized treatment—vaccine versus placebo—there also exists a continuous post-randomization exposure—exposure to pregnancy—characterized by the timing of conception. Given the seasonality of malaria transmission and waning of vaccine-induced immunity, vaccine effectiveness during pregnancy may vary with conception timing, motivating causal evaluations that consider both exposures. We propose methodological approaches for causal inference in this setting, study their large-sample and finite-sample properties, and apply them to the malaria vaccine trial to provide a comprehensive assessment of vaccine effectiveness against pregnancy malaria in the target subpopulation while accounting for temporal variation in disease transmission.
Hu et al. (Wed,) studied this question.