Causal Inference | Professor Tengyuan Liang

Deep Neural Networks for Estimation and Inference

Max H. Farrell, Tengyuan Liang, Sanjog Misra Econometrica

Can deep neural networks with standard archtectures estimate treatment effects and perform downstream uncertainty quantification tasks?

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 The Causal Shift

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 The Causal Shift