Standard experimental designs are geared toward point estimation and hypothesis testing,
while bandit algorithms are geared toward in‐sample outcomes. Here, we instead consider …

In recent years, microeconometrics experienced the 'credibility revolution', culminating in the
2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens. This 'revolution'in how to …

How to allocate vaccines over heterogeneous individuals is one of the important policy
decisions in pandemic times. This paper develops a procedure to estimate an individualized …

Dynamic treatment regimes are treatment allocations tailored to heterogeneous individuals
(eg, via previous outcomes and covariates). The optimal dynamic treatment regime is a …

We present a distribution optimization framework that significantly improves confidence
bounds for various risk measures compared to previous methods. Our framework …

Economic policies often involve dynamic interventions, where individuals receive repeated
interventions over multiple periods. This dynamics makes past responses informative to …

We study the problem of a decision maker who must provide the best possible treatment
recommendation based on an experiment. The desirability of the outcome distribution …

Static supervised learning—in which experimental data serves as a training sample for the
estimation of an optimal treatment assignment policy—is a commonly assumed framework of …

Dynamic decisions are pivotal to economic policy making. We show how existing evidence
from randomized control trials can be utilized to guide personalized decisions in challenging …

A decision maker typically (i) incorporates training data to learn about the relative
effectiveness of the treatments, and (ii) chooses an implementation mechanism that implies …