2 citations · 6 across the 9 of their papers we have counts for
10 papers
Rerandomization for quantile treatment effects
Tingxuan Han, Yuhao Wang
Although complete randomization is widely regarded as the gold standard for causal inference, covariate imbalance can still arise by chance in finite samples. Rerandomization has e…
Permutation Inference under Multi-way Clustering and Missing Data
Wenxuan Guo, Panos Toulis, Yuhao Wang
Econometric applications with multi-way clustering often feature a small number of effective clusters or heavy-tailed data, making standard cluster-robust and bootstrap inference u…
A multivariate extension of Azadkia-Chatterjee's rank coefficient
Wenjie Huang, Zonghan Li, Yuhao Wang
The Azadkia-Chatterjee coefficient is a rank-based measure of dependence between a random variable and a random vector . In…
Latent confounding in high-dimensional nonlinear models
Yuhao Wang, Rajen Shah
We consider the the problem of identifying causal effects given a high-dimensional treatment vector in the presence of low-dimensional latent confounders. We assume a parametric st…
Optimal structure learning and conditional independence testing
Ming Gao, Yuhao Wang, Bryon Aragam
We establish a fundamental connection between optimal structure learning and optimal conditional independence testing by showing that the minimax optimal rate for structure learnin…
Toward Universal Laws of Outlier Propagation
Aram Ebtekar, Yuhao Wang, Dominik Janzing
When a variety of anomalous features motivate flagging different samples as outliers, Algorithmic Information Theory (AIT) offers a principled way to unify them in terms of a sampl…
Adjusting auxiliary variables under approximate neighborhood interference
Xin Lu, Yuhao Wang, Zhiheng Zhang
Randomized experiments are the gold standard for causal inference. However, traditional assumptions, such as the Stable Unit Treatment Value Assumption (SUTVA), often fail in real-…
Minimax estimation of functionals in sparse vector model with correlated observations
Yuhao Wang, Pengkun Yang, Alexandre B. Tsybakov
We consider the observations of an unknown -sparse vector corrupted by Gaussian noise with zero mean and unknown covariance matrix . We propos…
Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
Xin Lu, Fan Yang, Yuhao Wang
Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to inclu…
Residual permutation test for regression coefficient testing
Kaiyue Wen, Tengyao Wang, Yuhao Wang
We consider the problem of testing whether a single coefficient is equal to zero in linear models when the dimension of covariates can be up to a constant fraction of sample si…