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.
Econometrics (econ.EM)
A Convexified Matching Approach to Imputation and Individualized Inference
We introduce a new convexified matching method for missing value imputation and individualized inference inspired by computational optimal transport.
Detecting Weak Distribution Shifts via Displacement Interpolation
Detecting weak, systematic distribution shifts and quantitatively modeling individual, heterogeneous responses to policies or incentives have found increasing empirical applications in social and economic sciences. We propose a model for weak distribution shifts via displacement interpolation, drawing from the optimal transport theory.
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?