In a special workshop in ACIC 2018, we were invited to analyze a simulated dataset to detect treatment effect heterogeneity. This article reports our results presented in the workshop. We also tried out more recent selective inference methods based …
We propose a falsification test for the IV assumptions using sub-populations of the data with overwhelming proportion of treated or untreated units. If the IV assumptions hold, we should find the intention-to-treat effect is zero within these …
This research letter proposes a new diagnostic plot for IV analysis, so large bias ratios (compared to OLS estimator) are not over-interpreted when the covariate is unrelated to the outcome.
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.