Machine Learning and Causality: The Impact of Financial Crises on Growth

Author/Editor:

Andrew J Tiffin

Publication Date:

November 1, 2019

Electronic Access:

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Disclaimer: 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.

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.

Series:

Working Paper No. 19/228

English

Publication Date:

November 1, 2019

ISBN/ISSN:

9781513518305/1018-5941

Stock No:

WPIEA2019228

Price:

$18.00 (Academic Rate:$18.00)

Format:

Paper

Pages:

30

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