369 citations · 375 across the 6 of their papers we have counts for
6 papers
Disentangled Feature Importance
Jin-Hong Du, Kathryn Roeder, Larry Wasserman
When predictors are statistically dependent, the appropriate definition of feature importance depends on the operational goal. Conditional-incremental measures are well-suited for…
Active multiple testing with proxy p-values and e-values
Ziyu Xu, Catherine Wang, Larry Wasserman +2
Researchers often lack the resources to test every hypothesis of interest directly or compute test statistics comprehensively, but often possess auxiliary data from which we can co…
Assumption-Lean Post-Integrated Inference with Surrogate Control Outcomes
Jin-Hong Du, Kathryn Roeder, Larry Wasserman
Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variations, such as batch effects and unmeasured covariates, ac…
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy +2
With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-c…
Simultaneous inference for generalized linear models with unmeasured confounders
Jin-Hong Du, Larry Wasserman, Kathryn Roeder
Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, ma…
High-dimensional variable selection
Larry Wasserman, Kathryn Roeder
This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the…