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By interpreting exporters' dynamics as a complex learning process, this paperconstitutes the first attempt to investigate the effectiveness of differentMachine Learning (ML) techniques in predicting firms' trade status. We focus onthe probability of Colombian firms surviving in the export market under twodifferent scenarios: a COVID-19 setting and a non-COVID-19 counterfactualsituation. By comparing the resulting predictions, we estimate the individualtreatment effect of the COVID-19 shock on firms' outcomes. Finally, we userecursive partitioning methods to identify subgroups with differentialtreatment effects. We find that, besides the temporal dimension, the mainfactors predicting treatment heterogeneity are interactions between firm sizeand industry.

Assessing the Impact of COVID-19 on Trade: a Machine Learning Counterfactual Analysis