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 …

Policy makers, firms, and researchers often choose among multiple options based on
estimates. Sampling error in the estimates used to guide choice leads to a winner's curse …

This paper studies a penalized statistical decision rule for the treatment assignment
problem. Consider the setting of a utilitarian policy maker who must use sample data to …

The increasing availability of individual-level data has led to numerous applications of
individualized (or personalized) treatment rules (ITRs). Policy makers often wish to …

One of the major concerns of targeting interventions on individuals in social welfare
programs is discrimination: individualized treatments may induce disparities across sensitive …

When designing eligibility criteria for welfare programs, policymakers naturally want to target
the individuals who will benefit the most. This paper extends the previous literature on …

Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials
is one key solution to personalized medicine and has received more and more attention in …

Understanding the impact of the most effective policies or treatments on a response variable
of interest is desirable in many empirical works in economics, statistics and other disciplines …

This paper studies experimental designs for estimation and inference on welfaremaximizing
policies in the presence of spillover effects. I consider a setting where units are organized …

Consider a causal structure with endogeneity (ie, unobserved confoundedness) in empirical
data, where an instrumental variable is available. In this setting, we show that the mean …