JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1118 10.6339/24-JDS1118 Data Science in Action Impacts of COVID-19 on Public Universities in Brazil: A Machine Learning Counterfactual Analysis RossiR.Jr.romeu.rossi@ufv.brromeu.rossi@ufv.br1 Universidade Federal de Viçosa - Instituto de Ciências Exatas e Tecnológicas - CAF, LMG818 Km6, Minas Gerais, Florestal 35690-000, Brazil 2024522024224621630 Supplementary Material

The following files are included in the supplementary material: (1) Study code file; (2) URL to INEP census data; (3) IBGE population of each municipality data.

29920231712024 2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2024 Open access article under the CC BY license.

This study delves into the impact of the COVID-19 pandemic on the enrollment rates of on-site undergraduate programs within Brazilian public universities. Employing the Machine Learning Control Method, a counterfactual scenario was constructed in which the pandemic did not occur. By contrasting this hypothetical scenario with real-world data on new entrants, a variable was defined to characterize the impact of the COVID-19 pandemic on on-site undergraduate programs at Brazilian public universities. This variable reveals that the impact factor varies significantly when considering the geographical locations of the institutions offering these courses. Courses offered by institutions located in smaller population cities experienced a more pronounced impact compared to those situated in larger urban centers.

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