Computational Applications to Behavioral Science
This report is a product of the collaboration between ideas42 and the Golub Capital Social Impact Lab to advance practical applications of machine learning to behavioral science policy and field experimentation.
The contents of this report are designed for any audience looking to learn more about how machine learning can add new techniques to behavioral design, causal inference, and experimentation. A big thank you to Schmidt Futures and The Alfred P. Sloan Foundation for their generous contributions to this innovative collaboration and research. This report is written in RMarkdown with