7 citations · 7 across the 3 of their papers we have counts for
11 papers
Experimental Design When N Equals One
Wenxuan Guo, Tengyuan Liang
N-of-1 trials, or time-series experiments, are widely used in clinical research and online platforms. Yet the theoretically optimal design for estimating many treatment effects rem…
Deep Learning for Individual Heterogeneity
Max H. Farrell, Tengyuan Liang, Sanjog Misra
This paper integrates deep neural networks (DNNs) into structural models to increase flexibility and capture rich heterogeneity while preserving interpretability. Economic (or scie…
A Markov Chain Approach to Preference Alignment
Takuya Koriyama, Tengyuan Liang
We propose Markov Chain from Human Feedback (MCHF), an elementary approach for aligning generative models from pairwise human preferences. Unlike Reinforcement Learning from Human…
Nonparametric Point Identification of Treatment Effect Distributions via Rank Stickiness
Tengyuan Liang
Treatment effect distributions are not identified without restrictions on the joint distribution of potential outcomes. Existing approaches either impose rank preservation -- a str…
No-Regret Generative Modeling via Parabolic Monge-Ampère PDE
Nabarun Deb, Tengyuan Liang
We introduce a novel generative modeling framework based on a discretized parabolic Monge-Ampère PDE, which emerges as a continuous limit of the Sinkhorn algorithm commonly used i…
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
Kulunu Dharmakeerthi, YoonHaeng Hur, Tengyuan Liang
Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shif…