Format:
1 Online-Ressource (circa 31 Seiten)
,
Illustrationen
ISBN:
9781513518305
Series Statement:
IMF working paper WP/19, 228
Content:
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
Additional Edition:
Erscheint auch als Druck-Ausgabe Tiffin, Andrew Machine Learning and Causality: The Impact of Financial Crises on Growth Washington, D.C. : International Monetary Fund, 2019 ISBN 9781513518305
Language:
English
Keywords:
Graue Literatur
DOI:
10.5089/9781513518305.001
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