Experimental Design | Professor Tengyuan Liang

Gaussianized Design Optimization for Covariate Balance in Randomized Experiments

Wenxuan Guo, Tengyuan Liang, Panos Toulis Journal of the Royal Statistical Society: Series B

This paper presents Gaussianized Design Optimization, a novel framework for optimally balancing covariates in experimental design.

Experimental Design Causal Inference Uncertainty Quantification The Causal Shift

Randomization Inference When N Equals One

Tengyuan Liang, Benjamin Recht Biometrika

A statistical theory for N-of-1 experiments, where a unit serves as its own control and treatment in rapid interleaving time windows.

Causal Inference Experimental Design Uncertainty Quantification The Causal Shift

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria

Tengyuan Liang Journal of Machine Learning Research

Blessings and curses of covariate shifts, directional convergece, and the connection to experimental design.

Statistical Learning Experimental Design The Distributional Regime The Causal Shift