IMF Working Papers

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

By Andrew J Tiffin

November 1, 2019

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Format: Chicago

Andrew J Tiffin. Machine Learning and Causality: The Impact of Financial Crises on Growth, (USA: International Monetary Fund, 2019) accessed September 18, 2024

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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.

Subject: Exchange rate flexibility, Financial crises, Foreign exchange, Machine learning, Technology

Keywords: B. machine learning, Banking crisis, Causal inference, Confidence interval, Counterfactual prediction, Exchange rate flexibility, Financial crisis, Global, Instrumental-variables approach, Machine learning, Machine learning tool, Machine-learning literature, Machine-learning model, Machine-learning modification, ML technique, Policy evaluation, Randomized experiments, RF algorithm, Supervised machine learning, Treatment effects, Treatment variable, WP

Publication Details

  • Pages:

    30

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

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  • Series:

    Working Paper No. 2019/228

  • Stock No:

    WPIEA2019228

  • ISBN:

    9781513518305

  • ISSN:

    1018-5941