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01953cas a2200313 a 4500 |
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|c 5.00 USD
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|z 9781513518305
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Tiffin, Andrew.
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|a Machine Learning and Causality :
|b The Impact of Financial Crises on Growth /
|c Andrew Tiffin.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2019.
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| 300 |
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|a 1 online resource (30 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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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|a Mode of access: Internet
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|a Confidence Interval
|2 imf
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| 650 |
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|a Instrumental-Variables Approach
|2 imf
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|a Machine-Learning Literature
|2 imf
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|a Treatment Variable
|2 imf
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|a WP
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|a Australia
|2 imf
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|a IMF Working Papers; Working Paper ;
|v No. 2019/228
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2019/228/001.2019.issue-228-en.xml
|z IMF e-Library
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