10 papers
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…
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…
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…