Causal Inference

Experimental Design When N Equals One

Wenxuan Guo, Tengyuan Liang arXiv preprint

A Markovian framework for experimental design in N-of-1 trials and switchback experiments, optimizing treatment assignment to minimize estimation error of treatment effects.

Nonparametric Point Identification of Treatment Effect Distributions via Rank Stickiness

Tengyuan Liang arXiv preprint

Identifying the entire treatment effect distribution via rank stickiness and Bregman-Sinkhorn copula. The conditional imputed outcome distribution is an exponential tilt of the marginal with a Bregman divergence as the exponent, yielding closed-form conditional moments and rank violation probabilities.

Causal Inference Optimal Transport The Causal Shift The Distributional Regime

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

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