English

Actionable Recourse via GANs for Mobile Health

Machine Learning 2022-11-15 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2211.06525,
  title  = {Actionable Recourse via GANs for Mobile Health},
  author = {Jennifer Chien and Anna Guitart and Ana Fernandez del Rio and Africa Perianez and Lauren Bellhouse},
  journal= {arXiv preprint arXiv:2211.06525},
  year   = {2022}
}

Comments

16 pages, formatted for extended abstract requirements

R2 v1 2026-06-28T05:42:52.050Z