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.
CausalInference
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.
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
Confounding can obfuscate the definition of the best prediction model (concept shift) and shift covariates to domains yet unseen (covariate shift). Therefore, a model maximizing prediction accuracy in the source environment could suffer a significant accuracy drop in the target environment. We propose a new domain adaptation method for observational data in the presence of confounding, and characterize the the stability and predictability tradeoff leveraging a structural causal model.
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.
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
Blessings and curses of covariate shifts, directional convergece, and the connection to experimental design.
Deep Neural Networks for Estimation and Inference
Can deep neural networks with standard archtectures estimate treatment effects and perform downstream uncertainty quantification tasks?