Detecting Weak Distribution Shifts via Displacement Interpolation

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.

May 2023 · YoonHaeng Hur, Tengyuan Liang

Universal Prediction Band via Semi-Definite Programming

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

March 2021 · Tengyuan Liang

Deep Neural Networks for Estimation and Inference

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

September 2018 · Max H. Farrell, Tengyuan Liang, Sanjog Misra