01953cas a2200313 a 4500 AALejournalIMF015969 230101c9999 xx r poo 0 0eng d 5.00 USD 9781513518305 1018-5941 BD-DhAAL BD-DhAAL Tiffin, Andrew. Machine Learning and Causality : The Impact of Financial Crises on Growth / Andrew Tiffin. Washington, D.C. : International Monetary Fund, 2019. 1 online resource (30 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Confidence Interval imf Instrumental-Variables Approach imf Machine-Learning Literature imf Treatment Variable imf WP imf Australia imf IMF Working Papers; Working Paper ; No. 2019/228 Full text available on IMF http://elibrary.imf.org/view/journals/001/2019/228/001.2019.issue-228-en.xml IMF e-Library