Genetics

Leverage Mendelian Randomization to Learn Meaningful Representations (LMR×2)

Mendelian randomization: Old and new insights

Mendelian randomization (MR) is a method for causal inference that utilizes the natural experiment in genetic inheritance. The idea of MR can be traced back to the dawn of modern statistics and genetics a century ago, although the terminology was not …

Mendelian randomization: Old and new insights

Mendelian randomization (MR) is a method for causal inference that utilizes the natural experiment in genetic inheritance. The idea of MR can be traced back to the dawn of modern statistics and genetics a century ago, although the terminology was not …

Permutation $p$-value approximation via generalized Stolarsky invariance

This paper uses a generalized Stolarsky's invariance principle to approximate the permutation $p$-value for two-sample linear test statistics. Along the way we discovered a simple probabilistic proof of Stolarsky's invariance principle.

Confounder adjustment in multiple hypothesis testing

Confounding introduces hidden bias to the statistical inference. We show in modern simultaneous testing, it is possible to correct for unmeasured confounders. Previous methods including SVA, LEAPP, RUV are unified in the same framework in this paper. …