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

Andrew Tiffin

No 2019/228, IMF Working Papers from International Monetary Fund

Abstract: 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.

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 (search for similar items in EconPapers)
Pages: 30
Date: 2019-11-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fdg
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Citations: View citations in EconPapers (18)

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