A treatment regime is a sequence of decision rules, one per decision point, that maps
accumulated patient information to a recommended intervention. An optimal treatment …

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 …

The conditional average treatment effect (CATE) is the best measure of individual causal
effects given baseline covariates. However, the CATE only captures the (conditional) …

This chapter introduces to readers the concept and methodology of reinforcement learning
and modern-day dynamic treatment regimes in statistics. This discussion should be of …

In this paper, we explore optimal treatment allocation policies that target distributional
welfare. Most literature on treatment choice has considered utilitarian welfare based on the …

The intersection of causal inference and machine learning for decision-making is rapidly
expanding, but the default decision criterion remains an average of individual causal …

Abstract Average Treatment Effect (ATE) estimation is a well-studied problem in causal
inference. However, it does not necessarily capture the heterogeneity in the data, and …

Policy makers need to decide whether to treat or not to treat heterogeneous individuals. The
optimal treatment choice depends on the welfare function that the policy maker has in mind …

Researchers often run resource-intensive randomized controlled trials (RCTs) to estimate
the causal effects of interventions on outcomes of interest. Yet these outcomes are often …

Recent years have seen the development of machine-learning algorithms that estimate
heterogeneous causal effects from randomized controlled trials. While the estimation of …