Machine Learning and Causality: The Impact of Financial Crises on Growth
31 Pages Posted: 3 Dec 2019
Date Written: November 2019
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example-assessing the impact of a hypothetical banking crisis on a country's growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
Keywords: Financial soundness indicators, Economic analysis, Financial crises, Banking crisis, Economic sectors, Supervised machine learning, causal inference, policy evaluation, counterfactual prediction, randomized experiments, treatment effects, financial crisis., WP, treatment effect, covariates, dataset, machine learn
JEL Classification: C21, C40, E6, E44, E50, E60, F3, G01, G20, E01, E6
Suggested Citation: Suggested Citation