6 citations · 11 across the 12 of their papers we have counts for
16 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…
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…
Exposure effects are not automatically useful for policymaking
Eric Auerbach, Jonathan Auerbach, Max Tabord-Meehan
We thank Savje (2023) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Savje recommends misspecified ex…
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…
Inference in Experiments with Matched Pairs and Imperfect Compliance
Yuehao Bai, Hongchang Guo, Azeem M. Shaikh +1
This paper studies inference for the local average treatment effect in randomized controlled trials with imperfect compliance where treatment status is determined according to "mat…
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…
Revisiting the Analysis of Matched-Pair and Stratified Experiments in the Presence of Attrition
Yuehao Bai, Meng Hsuan Hsieh, Jizhou Liu +1
In this paper we revisit some common recommendations regarding the analysis of matched-pair and stratified experimental designs in the presence of attrition. Our main objective is…
Inference for Matched Tuples and Fully Blocked Factorial Designs
Yuehao Bai, Jizhou Liu, Max Tabord-Meehan
This paper studies inference in randomized controlled trials with multiple treatments, where treatment status is determined according to a "matched tuples" design. Here, by a match…
Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes
Federico Bugni, Ivan Canay, Azeem Shaikh +1
This paper considers the problem of inference in cluster randomized experiments when cluster sizes are non-ignorable. Here, by a cluster randomized experiment, we mean one in which…
The Local Approach to Causal Inference under Network Interference
Eric Auerbach, Hongchang Guo, Max Tabord-Meehan
We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference desc…
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials
Max Tabord-Meehan
This paper proposes an adaptive randomization procedure for two-stage randomized controlled trials. The method uses data from a first-wave experiment in order to determine how to s…
Model Selection for Treatment Choice: Penalized Welfare Maximization
Eric Mbakop, Max Tabord-Meehan
This paper studies a penalized statistical decision rule for the treatment assignment problem. Consider the setting of a utilitarian policy maker who must use sample data to alloca…