Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in
an infinite-horizon decision problem, now refers to a general class of reinforcement learning …

Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …

Many applied decision-making problems have a dynamic component: The policymaker
needs not only to choose whom to treat, but also when to start which treatment. For example …

Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a
comprehensive introduction to statistical methodology for the evaluation and discovery of …

Decision makers, such as doctors and judges, make crucial decisions such as
recommending treatments to patients, and granting bails to defendants on a daily basis …

Machine learning (ML) has seen impressive growth in health science research due to its
capacity for handling complex data to perform a range of tasks, including unsupervised …

Dynamic treatment regimes (DTRs) are sequences of treatment decision rules, in which
treatment may be adapted over time in response to the changing course of an individual …

A main research goal in various studies is to use an observational data set and provide a
new set of counterfactual guidelines that can yield causal improvements. Dynamic …

Estimating dynamic treatment regimes (DTRs) from retrospective observational data is
challenging as some degree of unmeasured confounding is often expected. In this work, we …

In dynamic decision-making scenarios across business and healthcare, leveraging sample
trajectories from diverse populations can significantly enhance reinforcement learning (RL) …