2 citations · 3 across the 6 of their papers we have counts for
6 papers
Bayesian Mendelian randomization testing of interval causal null hypotheses: ternary decision rules and loss function calibration
Linyi Zou, Teresa Fazia, Hui Guo +1
Our approach to Mendelian Randomization (MR) analysis is designed to increase reproducibility of causal effect "discoveries" by: (i) using a Bayesian approach to inference; (ii) re…
Bayesian Mendelian randomization with study heterogeneity and data partitioning for large studies
Linyi Zou, Hui Guo, Carlo Berzuini
Background: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study hete…
Overlapping-sample Mendelian randomisation with multiple exposures: A Bayesian approach
Linyi Zou, Hui Guo, Carlo Berzuini
Background: Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate pu…
Mendelian Randomization with Incomplete Exposure Data: a Bayesian Approach
Teresa Fazia, Leonardo Egidi, Burcu Ayoglu +9
We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent…
Bayesian Mendelian Randomization
Carlo Berzuini, Hui Guo, Stephen Burgess +1
Our Bayesian approach to Mendelian Randomisation uses multiple instruments to assess the putative causal effect of an exposure on an outcome. The approach is robust to violations o…
Sufficient Covariate, Propensity Variable and Doubly Robust Estimation
Hui Guo, Philip Dawid, Giovanni Berzuini
Statistical causal inference from observational studies often requires adjustment for a possibly multi-dimensional variable, where dimension reduction is crucial. The propensity sc…