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Democratizing Policy Analytics with AutoML
Machine learning methods have made significant inroads in the social sciences. Computer algorithms now help scholars design cost-effective public policies, predict rare social events, and improve the allocation of funds. However, building and evaluating machine learning algorithms remain labor-intensive, error-prone tasks. Thus, areas that could benefit from modern computer algorithms are often held back owing to implementation challenges or lack of technical expertise. In this paper, I show how scholars can use automated machine learning (AutoML) tools to preprocess their data and create powerful estimation methods with minimal human input. I demonstrate the functionalities of three open-source, easy-to-use AutoML algorithms, and I replicate a well-designed forecasting model to highlight how researchers can achieve similar results with only a few lines of code.
This paper is one of seven published as part of the Policy Analytics Symposium.