9 papers
Graph-Laplacian Variance Estimators for Finely Stratified Experiments
Yuehao Bai, Xun Huang, Joseph P. Romano +2
This paper considers design-based inference on the average treatment effect in finely stratified experiments, where uncertainty arises only from the randomized treatment assignment…
Inference for Linear Systems with Unknown Coefficients
Yuehao Bai, Kirill Ponomarev, Andres Santos +3
This paper considers the problem of testing whether there exists a solution satisfying certain non-negativity constraints to a linear system of equations. Importantly and in contra…
On the Efficiency of Highly Stratified Experiments
Yuehao Bai, Jizhou Liu, Azeem M. Shaikh +1
This paper studies the use of highly stratified designs for the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments. By a…
Sharp Testable Implications of Encouragement Designs
Yuehao Bai, Shunzhuang Huang, Max Tabord-Meehan
This paper studies a potential outcome model with a continuous or discrete outcome, a discrete multi-valued treatment, and a discrete multi-valued instrument. We derive sharp, clos…
Inference in Cluster Randomized Trials with Matched Pairs
Yuehao Bai, Jizhou Liu, Azeem M. Shaikh +1
This paper studies inference in cluster randomized trials where treatment status is determined according to a "matched pairs" design. Here, by a cluster randomized experiment, we m…
A New Design-Based Variance Estimator for Finely Stratified Experiments
Yuehao Bai, Xun Huang, Joseph P. Romano +2
This paper considers the problem of design-based inference for the average treatment effect in finely stratified experiments. Here, by "design-based'' we mean that the only source…