• Corpus ID: 219687548

The leave-one-covariate-out conditional randomization test

@article{Katsevich2020TheLC,
  title={The leave-one-covariate-out conditional randomization test},
  author={Eugene Katsevich and Aaditya Ramdas},
  journal={arXiv: Methodology},
  year={2020}
}
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 we assume we know the joint distribution of the covariates, but assume nothing about the conditional distribution of the outcome given the covariates. Knockoffs is a popular methodology associated with this framework, but it suffers from two main drawbacks: only one-bit $p$-values are available for inference on each… 

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References

SHOWING 1-10 OF 49 REFERENCES

Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection

TLDR
This work proposes a new framework of ‘model‐X’ knockoffs, which reads from a different perspective the knockoff procedure that was originally designed for controlling the false discovery rate in linear models, and demonstrates the superior power of knockoffs through simulations.

A theoretical treatment of conditional independence testing under Model-X

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 methodological

Gene hunting with hidden Markov model knockoffs

TLDR
An exact and efficient algorithm is developed to sample knockoff variables in this setting and it is argued that, combined with the existing selective framework, this provides a natural and powerful tool for inference in genome‐wide association studies with guaranteed false discovery rate control.

Simultaneous high-probability bounds on the false discovery proportion in structured, regression and online settings

TLDR
The authors' finite-sample, closed form bounds are based on repurposing the FDP estimates from false discovery rate (FDR) controlling procedures designed for each of the above settings, and establish a novel connection between the parallel literatures of simultaneous FDP bounds and FDR control methods.

Teoria Statistica Delle Classi e Calcolo Delle Probabilità

  • The SAGE Encyclopedia of Research Design
  • 2022

Fast and Powerful Conditional Randomization Testing via Distillation

TLDR
Thedistilled~CRT is proposed, a novel approach to using state-of-the-art machine learning algorithms in the CRT while drastically reducing the number of times those algorithms need to be run, thereby taking advantage of their power and theCRT's statistical guarantees without suffering the usual computational expense.

Causal inference in genetic trio studies

TLDR
This work introduces a method to draw causal inferences—inferences immune to all possible confounding—from genetic data that include parents and offspring that is based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes.

Interactive martingale tests for the global null

TLDR
This work presents simple martingale analogs of these classical tests for global null testing, and develops a novel interactive test for the global null that can take advantage of covariates and repeated user guidance to create a data-adaptive ordering that achieves higher detection power against structured alternatives.

Multi-resolution localization of causal variants across the genome

TLDR
This work proposes KnockoffZoom, a non-parametric statistical method for the simultaneous discovery and fine-mapping of causal variants, assuming only that LD is described by hidden Markov models (HMMs).

The p‐filter: multilayer false discovery rate control for grouped hypotheses

In many practical applications of multiple testing, there are natural ways to partition the hypotheses into groups by using the structural, spatial or temporal relatedness of the hypotheses, and this