We extend the MR-RAPS method in our previous paper using the empirical partially Bayes framework described by Lindsay, allowing a true genome-wide design for Mendelian randomization.
This paper proposes a modified Cochran's $Q$ statistic to detect horizontal pleiotropy in Mendelian randomization. This extension is quite important when there are many weak genetic instruments.
We provide a comprehensive theoretical basis for two-sample summary-data Mendelian randomization. We find that horizontal pleiotropy is pervasive in MR studies. We propose a new method---robust adjusted profile score---that can consistently estimate …
Rosenbaum’s sensitivity analysis framework has several limitations: 1. It is mostly applicable to matched observational studies; 2. It only tests the sharp null hypothesis; 3. It assumes treatment effect homogeneity to obtain a confidence interval of …
Many modern IV studies (especially Mendelian Randomization) are carried out with the two-sample design, where the samples may come from different populations. We derive a new class of linear IV estimates that are robust to sample heterogeneity. We …
We approach the heterogeneous treatment effect problem in a novel way. Instead of trying to obtain the optimal treatment regime, we seek an interpretable model for effect modification using the recently developed selective inference framework.
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.
A crucial quantity in Rosenbaum’s sensitivity analysis is the 'sensitivity value', the amount of unmeasured confounding needed to alter the qualitative conclusions of an observational study. This paper looks into the properties of 'sensitivity value' …
We link Friedman's partial dependence plot with Pearl's backdoor adjustment formula. We discuss situations when possible causal interpretations can be made for black-box machine learning models.