Multiple Testing

Multiple conditional randomization tests

We propose a general framework for (multiple) conditional randomization tests that incorporate several important ideas in the recent literature. We establish a general sufficient condition on the construction of multiple conditional randomization …

Using sparsity to overcome unmeasured confounding

Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. This talk will explore another aspect—the utility of sparsity in model identifiability through two problems …

Using sparsity to overcome unmeasured confounding: Two examples

Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. This talk will explore another aspect—the utility of sparsity in model identifiability through two problems …

Multiple testing when many $p$-values are uniformly conservative

Qualitative interaction is an extreme form of treatment effect heterogeneity where the treatment can be beneficial for some but harmful for others. We formulated this question as a global testing problem with many conservative null $p$-values and …

Cross-screening in observational studies that test many hypotheses

This paper proposes a new method called 'cross-screening' to increase the power of sensitivity analysis when multiple causal hypotheses need to be tested simultaneously.

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. …