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

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  • Mr. Andrew J Tiffin

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.

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  • Mr. Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 2019/228, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2019/228
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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Mr. Andrew J Tiffin, 2016. "Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon," IMF Working Papers 2016/056, International Monetary Fund.
    3. Narcissa Balta & Plamen Nikolov, 2013. "Financial dependence and growth since the crisis," Quarterly Report on the Euro Area (QREA), Directorate General Economic and Financial Affairs (DG ECFIN), European Commission, vol. 12(3), pages 7-18, October.
    4. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    5. Edwards, Sebastian & Levy Yeyati, Eduardo, 2005. "Flexible exchange rates as shock absorbers," European Economic Review, Elsevier, vol. 49(8), pages 2079-2105, November.
    6. Demirguc-Kunt, Asli & Detragiache, Enrica & Gupta, Poonam, 2006. "Inside the crisis: An empirical analysis of banking systems in distress," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 702-718, August.
    7. Rajan, Raghuram G & Zingales, Luigi, 1998. "Financial Dependence and Growth," American Economic Review, American Economic Association, vol. 88(3), pages 559-586, June.
    8. Swati R. Ghosh & Atish R. Ghosh, 2003. "Structural Vulnerabilities and Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 50(3), pages 1-7.
    9. Hutchison, Michael M & Noy, Ilan, 2005. "How Bad Are Twins? Output Costs of Currency and Banking Crises," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(4), pages 725-752, August.
    10. Mr. Luc Laeven & Mr. Fabian Valencia, 2018. "Systemic Banking Crises Revisited," IMF Working Papers 2018/206, International Monetary Fund.
    11. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    12. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    13. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    14. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    15. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    16. Mr. Abdul d Abiad & Ms. Petya Koeva Brooks & Ms. Irina Tytell & Mr. Daniel Leigh & Mr. Ravi Balakrishnan, 2009. "What’s the Damage? Medium-term Output Dynamics After Banking Crises," IMF Working Papers 2009/245, International Monetary Fund.
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    2. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    3. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," WiSo-HH Working Paper Series 62, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
    4. Hongqin Tang & Jianping Zhu & Nan Li & Weipeng Wu, 2024. "Impact of Enterprise Supply Chain Digitalization on Cost of Debt: A Four-Flows Perspective Analysis Using Explainable Machine Learning Methodology," Sustainability, MDPI, vol. 16(19), pages 1-27, October.
    5. Anastasios Petropoulos & Vasilis Siakoulis & Evangelos Stavroulakis, 2022. "Towards an early warning system for sovereign defaults leveraging on machine learning methodologies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 118-129, April.
    6. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
    7. Andrew Tsang, 2024. "Uncovering heterogeneous regional impacts of Chinese monetary policy," Empirical Economics, Springer, vol. 67(3), pages 915-940, September.
    8. Kelvin Mulungu & Zewdu Ayalew Abro & Wambui Beatrice Muriithi & Menale Kassie & Miachael Kidoido & Subramanian Sevgan & Samira Mohamed & Chrysantus Tanga & Fathiya Khamis, 2024. "One size does not fit all: Heterogeneous economic impact of integrated pest management practices for mango fruit flies in Kenya—a machine learning approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 261-279, February.
    9. Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    10. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    11. Christian Stetter & Philipp Mennig & Johannes Sauer, 2022. "Using Machine Learning to Identify Heterogeneous Impacts of Agri-Environment Schemes in the EU: A Case Study," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(4), pages 723-759.
    12. Kaiwen Hou & David Hou & Yang Ouyang & Lulu Zhang & Aster Liu, 2022. "Crises Do Not Cause Lower Short-Term Growth," Papers 2211.04558, arXiv.org, revised Nov 2022.
    13. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    14. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    15. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.

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