  Mobile health apps provide a unique means of collecting data that can be used
to deliver adaptive interventions.The predicted outcomes considerably influence
the selection of such interventions. Recourse via counterfactuals provides
tangible mechanisms to modify user predictions. By identifying plausible
actions that increase the likelihood of a desired prediction, stakeholders are
afforded agency over their predictions. Furthermore, recourse mechanisms enable
counterfactual reasoning that can help provide insights into candidates for
causal interventional features. We demonstrate the feasibility of GAN-generated
recourse for mobile health applications on ensemble-survival-analysis-based
prediction of medium-term engagement in the Safe Delivery App, a digital
training tool for skilled birth attendants.
