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Found 10 papers, 2 papers with code
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Dental disease is one of the most common chronic diseases despite being largely preventable.
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education.
Estimating causal effects with optimization-based methods: A review and empirical comparison
In the absence of randomized controlled and natural experiments, it is necessary to balance the distributions of (observable) covariates of the treated and control groups in order to obtain an unbiased estimate of a causal effect of interest; otherwise, a different effect size may be estimated, and incorrect recommendations may be given.
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning
In batch reinforcement learning, there can be poorly explored state-action pairs resulting in poorly learned, inaccurate models and poorly performing associated policies.
Statistical Inference with M-Estimators on Adaptively Collected Data
Yet there is a lack of general methods for conducting statistical inference using more complex models on data collected with (contextual) bandit algorithms; for example, current methods cannot be used for valid inference on parameters in a logistic regression model for a binary reward.
Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health
For example, a mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users.
Inference for Batched Bandits
As bandit algorithms are increasingly utilized in scientific studies and industrial applications, there is an associated increasing need for reliable inference methods based on the resulting adaptively-collected data.
iSurvive: An Interpretable, Event-time Prediction Model for mHealth
We present a parameter learning method for GLM emissions and survival model fitting, and present promising results on both synthetic data and an mHealth drug use dataset.
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions
Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions.
Statistical Inference in Dynamic Treatment Regimes
We then review an interesting challenge, that of nonregularity that often arises in this area.
