Working Paper

Democratizing Policy Analytics with AutoML

March 25, 2021

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.

Read more [1].

This paper is one of seven published as part of the Policy Analytics Symposium [2]. 


Source URL:https://www.mercatus.org/publications/regulation/democratizing-policy-analytics-automl

Links
[1] https://www.mercatus.org/system/files/freire_-_working_paper_-_democratizing_policy_analytics_with_automl_-_v1.pdf [2] https://www.mercatus.org/publications/policy-analytics/policy-analytics-symposium

https://www.mercatus.org