Bayesian experimental design (BED) provides a powerful and general framework for
optimizing the design of experiments. However, its deployment often poses substantial …

Adaptive experimental designs can dramatically improve efficiency in randomized trials. But
with adaptively collected data, common estimators based on sample means and inverse …

Reinforcement learning algorithms describe how an agent can learn an optimal action policy
in a sequential decision process, through repeated experience. In a given environment, the …

School closures affecting more than 1.5 billion children are designed to prevent the spread
of current public health risks from the COVID-19 pandemic, but they simultaneously …

Factorial designs are widely used to study multiple treatments in one experiment. While t-
tests using a fully-saturated “long” model provide valid inferences,“short” model t-tests (that …

We introduce an adaptive targeted treatment assignment methodology for field experiments.
Our Tempered Thompson Algorithm balances the goals of maximizing the precision of …

Bandit algorithms are increasingly used in real-world sequential decision-making problems.
Associated with this is an increased desire to be able to use the resulting datasets to answer …

Governments increasingly use randomized controlled trials (RCTs) to test innovations, yet
we know little about how they incorporate results into policymaking. We study 30 US cities …

In October 2019, Abhijit Banerjee, Esther Duflo, and Michael Kremer jointly won the 51st
Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel" for their …

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 …