7 citations · 19 across the 9 of their papers we have counts for
11 papers
Imputation is all you need: double robustness, semiparametric efficiency, and automatic covariate balance for estimating the average treatment effect
Fang Han, Peng Ding
Imputation-based causal estimation is typically viewed as relying exclusively on an outcome model, in contrast to augmented inverse-probability weighting, whose consistency is prot…
Generative bootstrap processes
Ziming Lin, Fang Han
We study generative bootstrap processes obtained by resampling from fitted generative distributions. We establish necessary and sufficient conditions for their conditional weak con…
Limit theorems of Azadkia-Chatterjee's conditional graph correlation
Muhong Gao, Fang Han, Qizhai Li
Inferring the strength of conditional dependence and testing conditional independence are fundamental problems in statistics. A recent breakthrough by Azadkia and Chatterjee introd…
Limiting spectral distributions of large consistent rank correlation matrices
Zhaorui Dong, Fang Han, Jianfeng Yao
We study random matrices whose entries are obtained by applying consistent rank correlations, such as Hoeffding's , pairwise to a high-dimensional random vector with mutually in…
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, to measure covariate s…
On the failure of the bootstrap for Chatterjee's rank correlation
Zhexiao Lin, Fang Han
While researchers commonly use the bootstrap for statistical inference, many of us have realized that the standard bootstrap, in general, does not work for Chatterjee's rank correl…
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 combine imputation an…
Azadkia-Chatterjee's correlation coefficient adapts to manifold data
Fang Han, Zhihan Huang
In their seminal work, Azadkia and Chatterjee (2021) initiated graph-based methods for measuring variable dependence strength. By appealing to nearest neighbor graphs, they gave an…
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 awaited by many. This pap…
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 paper, Abadie and Imbens…
On the power of Chatterjee rank correlation
Hongjian Shi, Mathias Drton, Fang Han
Chatterjee (2021) introduced a simple new rank correlation coefficient that has attracted much recent attention. The coefficient has the unusual appeal that it not only estimates a…