BayesMR: A Bayesian Mendelian Randomization Approach

The output of the method is a posterior distribution over the target causal effect, which provides an immediate and easily interpretable measure of the uncertainty in the estimation. More importantly, with the BayesMR algorithm we can estimate the model evidence for both directions to determine how much more likely the expected direction is relative to the reverse direction. The data set contains source code implementing the BayesMR algorithm, which is described in the article titled "Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach"(doi.org/10.1177/0962280219851817) by Ioan Gabriel Bucur, Tom Claassen and Tom Heskes.

The data set also contains simulated data necessary for exactly reproducing the figures in the article as well as the routines necessary for recreating it. This research is presented in Chapter 4 of the PhD thesis titled "Being Bayesian about Causal Inference" by Ioan Gabriel Bucur. The code is written in the R and C++ programming languages.