68 citations · 149 across the 8 of their papers we have counts for
8 papers
Nonlinear Causal Discovery with Confounders
Chunlin Li, Xiaotong Shen, Wei Pan
This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive…
Flexible Instrumental Variable Models With Bayesian Additive Regression Trees
Charles Spanbauer, Wei Pan
Methods utilizing instrumental variables have been a fundamental statistical approach to estimation in the presence of unmeasured confounding, usually occurring in non-randomized o…
Inference of nonlinear causal effects with GWAS summary data
Ben Dai, Chunlin Li, Haoran Xue +2
Large-scale genome-wide association studies (GWAS) have offered an exciting opportunity to discover putative causal genes or risk factors associated with diseases by using SNPs as…
Inference for a Large Directed Acyclic Graph with Unspecified Interventions
Chunlin Li, Xiaotong Shen, Wei Pan
Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized…
Significance tests of feature relevance for a black-box learner
Ben Dai, Xiaotong Shen, Wei Pan
An exciting recent development is the uptake of deep neural networks in many scientific fields, where the main objective is outcome prediction with the black-box nature. Significan…
Asymptotically Independent U-Statistics in High-Dimensional Testing
Yinqiu He, Gongjun Xu, Chong Wu +1
Many high-dimensional hypothesis tests aim to globally examine marginal or low-dimensional features of a high-dimensional joint distribution, such as testing of mean vectors, covar…