Machine Learning and Causality: The Impact of Financial Crises on Growth
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- 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.
- 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).
- Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
- 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.
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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;All these keywords.
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2021-02-08 (Big Data)
- NEP-CMP-2021-02-08 (Computational Economics)
- NEP-FDG-2021-02-08 (Financial Development and Growth)
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