In clinical trials randomizing participants to active vs. control conditions and following units until the occurrence of a primary clinical endpoint, evaluating the efficacy of a quantitative treatment (e.g., drug dosage) is often difficult. Stochastic-interventional effects, which measure the causal effect of perturbing the treatment’s observed value, provide an interpretable solution; yet, their use in vaccine trials requires care, for such trials measure immunologic biomarkers – useful for understanding the mechanisms by which vaccines confer protection or as surrogate endpoints – via outcome-dependent two-phase sampling (e.g., case-cohort) designs. These biased sampling designs have earned their popularity: they circumvent the economic burden of measuring biomarkers on all study units without limiting opportunities to detect mechanistically informative biomarkers. We discuss a semiparametric biased sampling correction allowing for asymptotically efficient inference on a causal vaccine efficacy measure, defined by contrasting assignments of study units to active vs. control while also shifting observed biomarker expression in the active condition, yielding a causal dose-response analysis informative of next-generation vaccine efficacy and of transporting efficacy from a source pathogen strain (e.g., SARS-CoV-2 at outbreak) to variants of concern (e.g., Omicron BA.5). We present the results of applying this approach in the Moderna COVE COVID-19 vaccine efficacy trial.