Super learning in the SAS system
Background and objective: Stacking is an ensemble machine learning method that averages predictions from multiple other algorithms, such as generalized linear models and regression trees. A recent iteration of stacking, called super learning, has been developed as a general approach to black box supervised learning and has seen frequent usage, in part due to the availability of an R package. I develop super learning in the SAS software system using a new macro, and demonstrate its performance relative to the R package. 
Methods: I follow closely previous work using the R SuperLearner package and assess the performance of super learning in a number of domains. I compare the R package with the new SAS macro in a small set of simulations assessing curve fitting in a prediction model, a set of 14 publicly available datasets to assess cross-validated, expected loss, and data from a randomized trial of job seekers' training to assess the utility of super learning in causal inference using inverse probability weighting. 
Results: Across the simulated data and the publicly available data, the macro performed similarly to the R package, even with a different set of potential algorithms available natively in R and SAS. The example with inverse probability weighting demonstrated the ability of the SAS macro to include algorithms developed in R. 
Conclusions: The super learner macro performs as well as the R package at a number of tasks. Further, by extending the macro to include the use of R packages, the macro can leverage both the robust, enterprise oriented procedures in SAS and the nimble, cutting edge packages in R. In the spirit of ensemble learning, this macro extends the potential library of algorithms beyond a single software system and provides a simple avenue into machine learning in SAS.