1 citations · 2 across the 12 of their papers we have counts for
14 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…
Inference for Treatment Effects Conditional on Generalized Principal Strata using Instrumental Variables
Yuehao Bai, Shunzhuang Huang, Sarah Moon +3
We propose a general approach to inference for a broad class of models that arise in the analysis of treatment effects with discrete-valued treatments and instruments and a general…
On the Identifying Power of Generalized Monotonicity for Average Treatment Effects
Yuehao Bai, Shunzhuang Huang, Sarah Moon +2
In the context of a binary outcome, treatment, and instrument, Balke and Pearl (1993, 1997) es- tablish that the monotonicity condition of Imbens and Angrist (1994) has no identify…
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…
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…
Covariate Adjustment in Experiments with Matched Pairs
Yuehao Bai, Liang Jiang, Joseph P. Romano +2
This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to a…
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…
Optimality of Matched-Pair Designs in Randomized Controlled Trials
Yuehao Bai
In randomized controlled trials (RCTs), treatment is often assigned by stratified randomization. I show that among all stratified randomization schemes which treat all units with p…
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…