Precision medicine seeks to maximize the quality of health care by individualizing the health-
care process to the uniquely evolving health status of each patient. This endeavor spans a …

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 …

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical
applications, such as personalized medicine and optimal resource allocation. In this article …

In many areas, practitioners seek to use observational data to learn a treatment assignment
policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or …

There has been considerable interest across several fields in methods that reduce the
problem of learning good treatment assignment policies to the problem of accurate policy …

This book builds on and is a sequel to our book Targeted Learning: Causal Inference for
Observational and Experimental Studies (2011). Since the publication of this first book on …

It is well known that both the direction and magnitude of the treatment effect in clinical trials
are often affected by baseline patient characteristics (generally referred to as biomarkers) …

This book was written to summarize and describe the state of the art of statistical methods
developed to address questions of estimation and inference for dynamic treatment regimes …

In many settings, a decision maker wishes to learn a rule, or policy, that maps from
observable characteristics of an individual to an action. Examples include selecting offers …

Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that
can adapt over time to an evolving illness. The goal is to accommodate heterogeneity …