The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal inference. One popular approach is to utilize these many-dimensional measurements as instrumental variables (instruments) for improving the causal effect estimate between other pairs of variables. Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid. We propose a general and efficient causal inference algorithm (MASSIVE) consisting of Model Assessment and Stochastic Search for Instrumental Variable Estimation. The MASSIVE algorithm accounts for model uncertainty by performing Bayesian model averaging over the most promising many dimensional instrumental variable models, while at the same time employing weaker assumptions regarding the data generating process compared to similar methods.The data set contains source code implementing the MASSIVE algorithm, which is described in the article titled "MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models"(http://proceedings.mlr.press/v124/gabriel-bucur20a.html) by Ioan Gabriel Bucur, Tom Claassen and Tom Heskes. The data set also contains simulated data necessary for reproducing the figures in the article as well as routines necessary for recreating it. This research is presented in Chapter 5 of the PhD thesis titled "Being Bayesian about Causal Inference" byIoan Gabriel Bucur. The code is written in the R and C++ programming languages.