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Machine Learning and Causality: The Impact of Financial Crises on Growth
Author:
Mr. Andrew J Tiffin
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Series:
IMF Working Papers
Volume/Issue:
Volume 2019: Issue 228
Language:
English
Publication Date:
01 Nov 2019
DOI:
https://doi.org/10.5089/9781513518305.001
ISBN:
9781513518305
ISSN:
1018-5941
Pages:
30
Keywords:
WP; machine-learning literature; instrumental-variables approach; treatment variable; confidence interval; ML technique; Supervised machine learning; causal inference; policy evaluation; counterfactual prediction; randomized experiments; treatment effects; banking crisis; financial crisis; B. machine learning; machine learning tool; machine-learning modification; RF algorithm; machine-learning model; Machine learning; Exchange rate flexibility; Global
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IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
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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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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund