34 citations · 53 across the 6 of their papers we have counts for
6 papers
Location Tests with Noisy Proxies for Latent Variables
Louis Deutsch, Eugene Katsevich
We investigate inference in a latent binary variable model where a noisy proxy of the latent variable is available, motivated by the variable perturbation effectiveness problem in…
Large-scale simultaneous inference under dependence
Jinjin Tian, Xu Chen, Eugene Katsevich +2
Simultaneous inference allows for the exploration of data while deciding on criteria for proclaiming discoveries. It was recently proved that all admissible post-hoc inference meth…
The leave-one-covariate-out conditional randomization test
Eugene Katsevich, Aaditya Ramdas
Conditional independence testing is an important problem, yet provably hard without assumptions. One of the assumptions that has become popular of late is called "model-X", where w…
Fast and Powerful Conditional Randomization Testing via Distillation
Molei Liu, Eugene Katsevich, Lucas Janson +1
We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y is independent of X given Z. The conditiona…
On the power of conditional independence testing under model-X
Eugene Katsevich, Aaditya Ramdas
For testing conditional independence (CI) of a response Y and a predictor X given covariates Z, the recently introduced model-X (MX) framework has been the subject of active method…
Simultaneous high-probability bounds on the false discovery proportion in structured, regression, and online settings
Eugene Katsevich, Aaditya Ramdas
While traditional multiple testing procedures prohibit adaptive analysis choices made by users, Goeman and Solari (2011) proposed a simultaneous inference framework that allows use…