9 papers · 1 filter
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…
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…
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…
Testing the Fairness-Accuracy Improvability of Algorithms
Eric Auerbach, Annie Liang, Kyohei Okumura +1
Many organizations use algorithms that have a disparate impact, i.e., the benefits or harms of the algorithm fall disproportionately on certain social groups. Addressing an algorit…
A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances
Yuehao Bai, Azeem M. Shaikh, Max Tabord-Meehan
The past two decades have witnessed a surge of new research in the analysis of randomized experiments. The emergence of this literature may seem surprising given the widespread use…