  By interpreting exporters' dynamics as a complex learning process, this paper
constitutes the first attempt to investigate the effectiveness of different
Machine Learning (ML) techniques in predicting firms' trade status. We focus on
the probability of Colombian firms surviving in the export market under two
different scenarios: a COVID-19 setting and a non-COVID-19 counterfactual
situation. By comparing the resulting predictions, we estimate the individual
treatment effect of the COVID-19 shock on firms' outcomes. Finally, we use
recursive partitioning methods to identify subgroups with differential
treatment effects. We find that, besides the temporal dimension, the main
factors predicting treatment heterogeneity are interactions between firm size
and industry.
