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?

Universal Prediction Band via Semi-Definite Programming

Tengyuan Liang Journal of the Royal Statistical Society: Series B

This paper proposes a computationally efficient method to construct nonparametric, heteroscedastic prediction bands for uncertainty quantification.

Uncertainty Quantification The Causal Shift The Interpolation Regime

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 Causal Shift

Detecting Weak Distribution Shifts via Displacement Interpolation

YoonHaeng Hur, Tengyuan Liang Journal of Business & Economic Statistics

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.

Statistical Inference Optimal Transport 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

Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction

Kulunu Dharmakeerthi, YoonHaeng Hur, Tengyuan Liang arXiv preprint

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.

Statistical Learning The Causal Shift

A Convexified Matching Approach to Imputation and Individualized Inference

YoonHaeng Hur, Tengyuan Liang arXiv preprint

We introduce a new convexified matching method for missing value imputation and individualized inference inspired by computational optimal transport.

Statistical Inference Optimal Transport 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