Actionable Recourse via GANs for Mobile Health
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 12 Nov 2022.
Builds on
17 of 49 references.
References · 17
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5 more
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