Misclassification between causes of death can produce bias in estimated cumulative incidence functions. When estimating causal quantities, such as comparing the cumulative incidence of death due to specific causes under interventions, such bias can lead to suboptimal decision making. Here, a consistent semiparametric estimator of the cumulative incidence function under interventions in settings with misclassification between two event types is presented. The measurement parameters for this estimator can be informed by validation data or expert knowledge. Moreover, a modified bootstrap approach to variance estimation is proposed for confidence interval construction. The proposed estimator was applied to estimate the cumulative incidence of AIDS-related mortality in the Multicenter AIDS Cohort Study under single- versus combination-drug antiretroviral therapy regimens that may be subject to confounding. The proposed estimator is shown to be consistent and performed well in finite samples via a series of simulation experiments.