Background Recent evidence suggests that there is often substantial variation in the benefits
and harms across a trial population. We aimed to identify regression modeling approaches …

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 …

Estimation of individual treatment effect in observational data is complicated due to the
challenges of confounding and selection bias. A useful inferential framework to address this …

The study of new medical treatments, and sequences of treatments, is inextricably linked
with statistics. Without statistical estimation and inference, we are left with case studies and …

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

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 …

Forest-based methods have recently gained in popularity for non-parametric treatment effect
estimation. Building on this line of work, we introduce causal survival forests, which can be …

This paper studies offline policy learning, which aims at utilizing observations collected a
priori (from either fixed or adaptively evolving behavior policies) to learn an optimal …

A treatment regime formalizes personalized medicine as a function from individual patient
characteristics to a recommended treatment. A high‐quality treatment regime can improve …