Zhexiao Lin

On regression-adjusted imputation estimators of the average treatment effect
Zhexiao Lin, Fang Han
Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that…

Variance reduction combining pre-experiment and in-experiment data
Zhexiao Lin, Pablo Crespo
Online controlled experiments (A/B testing) are essential in data-driven decision-making for many companies. Increasing the sensitivity of these experiments,…

On the Sequence Evaluation based on Stochastic Processes
Tianhao Zhang, Zhexiao Lin, Zhecheng Sheng, Chen Jiang, Dongyeop Kang
Generative models have gained significant prominence in Natural Language Processing (NLP), especially in tackling the complex task of modeling and evaluating…

Limit theorems of Chatterjee's rank correlation
Zhexiao Lin, Fang Han
Establishing the limiting distribution of Chatterjee's rank correlation for a general, possibly non-independent, pair of random variables has been eagerly…

On Rosenbaum's Rank-based Matching Estimator
Matias D. Cattaneo, Fang Han, Zhexiao Lin
In two influential contributions, Rosenbaum (2005, 2020) advocated for using the distances between component-wise ranks, instead of the original data values,…

Estimation based on nearest neighbor matching: from density ratio to average treatment effect
Zhexiao Lin, Peng Ding, Fang Han
Nearest neighbor (NN) matching as a tool to align data sampled from different groups is both conceptually natural and practically well-used. In a landmark…

On boosting the power of Chatterjee's rank correlation
Zhexiao Lin, Fang Han
Chatterjee (2021)'s ingenious approach to estimating a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly…

Topic-aware chatbot using Recurrent Neural Networks and Nonnegative Matrix Factorization
Yuchen Guo, Nicholas Hanoian, Zhexiao Lin, Nicholas Liskij, Hanbaek Lyu, Deanna Needell, Jiahao Qu, Henry Sojico, Yuliang Wang, Zhe Xiong, Zhenhong Zou
We propose a novel model for a topic-aware chatbot by combining the traditional Recurrent Neural Network (RNN) encoder-decoder model with a topic attention…