Machine Learning and Causality: The Impact of Financial Crises on Growth
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 leadi...
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| Format: | eBook |
| Language: | English |
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Washington, D.C.
International Monetary Fund
2019
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| Series: | IMF Working Papers
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| Collection: | International Monetary Fund - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Physical Description: | 30 pages |
| ISBN: | 9781513518305 |