We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted
treatment assignment policy, where the goal is to use a participant's survey responses to …

This paper studies identification and inference of the welfare gain that results from switching
from one policy (such as the status quo policy) to another policy. The welfare gain is not …

We propose a novel regression adjustment method designed for estimating distributional
treatment effect parameters in randomized experiments. Randomized experiments have been …

This paper studies how altruistic preferences are changed by markets and incentives. We
conduct a laboratory experiment with a within-subject design. Subjects are asked to choose …

When choosing whether and how much to donate, potential donors often observe a set of
default donation amounts known as an``ask string.''In an experiment with more than 400,000 …

In this paper, we address the issue of estimating and inferring the distributional treatment
effects in randomized experiments. The distributional treatment effect provides a more …

This dissertation has two main themes: treatment assignment rules and altruistic preferences.
The first two chapters are about comparing different treatment assignment rules using …

We propose a bootstrap‐based calibrated projection procedure to build confidence intervals
for single components and for smooth functions of a partially identified parameter vector in …

A growing number of empirical models exhibit set-valued predictions. This paper develops
a tractable inference method with finite-sample validity for such models. The proposed …

Contextual bandits (CBs) represent a fundamental yet highly impactful class of decision-making
problems that necessitate quantifying uncertainty in estimates from supervised learning …