# Actionable Recourse via GANs for Mobile Health

arXiv: https://arxiv.org/abs/2211.06525 · HTML: https://mlsanity.com/paper/2211.06525/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Algorithmic recourse under imperfect causal knowledge: a probabilistic approach](https://mlsanity.com/paper/2006.06831/) (2020)
- [Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems](https://mlsanity.com/paper/1907.09615/) (2019)
- [CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets](https://mlsanity.com/paper/2009.05199/) (2020)
- [Unbiased split variable selection for random survival forests using maximally selected rank statistics](https://mlsanity.com/paper/1605.03391/) (2016)
- [Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles](https://mlsanity.com/paper/1710.02264/) (2017)
- [Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs](https://mlsanity.com/paper/2107.02480/) (2021)
- [Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data](https://mlsanity.com/paper/1802.02301/) (2018)
- [A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries](https://mlsanity.com/paper/2111.01786/) (2021)
- [Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model](https://mlsanity.com/paper/1710.02262/) (2017)
- [The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition](https://mlsanity.com/paper/1901.05147/) (2019)
- [Efficient computation of counterfactual explanations of LVQ models](https://mlsanity.com/paper/1908.00735/) (2019)
- [Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks](https://mlsanity.com/paper/2010.02419/) (2020)
- [A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities](https://mlsanity.com/paper/2111.11203/) (2021)
- [User Engagement in Mobile Health Applications](https://mlsanity.com/paper/2206.08178/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recourse for reclamation: Chatting with generative language models](https://mlsanity.com/paper/2403.14467/) (2024)

## 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.