Gaussianized Design Optimization for Covariate Balance in Randomized Experiments
This paper presents Gaussianized Design Optimization, a novel framework for optimally balancing covariates in experimental design.
Uncertainty Quantification
Randomization Inference When N Equals One
A statistical theory for N-of-1 experiments, where a unit serves as its own control and treatment in rapid interleaving time windows.
Universal Prediction Band via Semi-Definite Programming
This paper proposes a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification.
Deep Neural Networks for Estimation and Inference
Can deep neural networks with standard archtectures estimate treatment effects and perform downstream uncertainty quantification tasks?