254 citations · 882 across the 66 of their papers we have counts for
5 papers · 1 filter
Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning"
Edward H. Kennedy, Sivaraman Balakrishnan, Larry A. Wasserman
We congratulate the authors on their exciting paper, which introduces a novel idea for assessing the estimation bias in causal estimates. Doubly robust estimators are now part of t…
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah +1
A number of applications require two-sample testing on ranked preference data. For instance, in crowdsourcing, there is a long-standing question of whether pairwise comparison data…
Minimax optimality of permutation tests
Ilmun Kim, Sivaraman Balakrishnan, Larry Wasserman
Permutation tests are widely used in statistics, providing a finite-sample guarantee on the type I error rate whenever the distribution of the samples under the null hypothesis is…
A Unified View of Label Shift Estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan +1
Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. There are two dominant approaches for estimating the label margin…
Minimax Optimal Conditional Independence Testing
Matey Neykov, Sivaraman Balakrishnan, Larry Wasserman
We consider the problem of conditional independence testing of and given where and are three real random variables and is continuous. We focus on two main…