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Prediction Policy Problems

Citations

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Cited by:

  1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
  2. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
  3. Bansak, Kirk & Martén, Linna, 2024. "Algorithmic Decision-Making, Fairness, and the Distribution of Impact: Application to Refugee Matching," SOFI Working Papers in Labour Economics 6/2024, Stockholm University, Swedish Institute for Social Research.
  4. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  5. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
  6. Kimia Keshanian & Narayan Ramasubbu & Kaushik Dutta, 2023. "Mobile advertisement campaigns for boosting in‐store visits: A design framework and case study," Production and Operations Management, Production and Operations Management Society, vol. 32(8), pages 2438-2454, August.
  7. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020. "Optimal data collection for randomized control trials," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
  8. Anja Lambrecht & Catherine Tucker, 2024. "Apparent algorithmic discrimination and real-time algorithmic learning in digital search advertising," Quantitative Marketing and Economics (QME), Springer, vol. 22(4), pages 357-387, December.
  9. Hidalgo, César A., 2023. "The policy implications of economic complexity," Research Policy, Elsevier, vol. 52(9).
  10. Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," Papers 2011.11017, arXiv.org.
  11. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
  12. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2023. "Taste of home: Birth town bias in Geographical Indications," Economics & Statistics Discussion Papers esdp23089, University of Molise, Department of Economics.
  13. van der Heijden, Hans, 2022. "Predicting industry sectors from financial statements: An illustration of machine learning in accounting research," The British Accounting Review, Elsevier, vol. 54(5).
  14. Chen, S. & Doerr, S. & Frost, J. & Gambacorta, L. & Shin, H.S., 2023. "The fintech gender gap," Journal of Financial Intermediation, Elsevier, vol. 54(C).
  15. Leuz, Christian, 2022. "Towards a design-based approach to accounting research," Journal of Accounting and Economics, Elsevier, vol. 74(2).
  16. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
  17. Newell, Richard G. & Prest, Brian C. & Sexton, Steven E., 2021. "The GDP-Temperature relationship: Implications for climate change damages," Journal of Environmental Economics and Management, Elsevier, vol. 108(C).
  18. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  19. Emanuel Kohlscheen, 2022. "Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices," Papers 2208.14254, arXiv.org, revised Oct 2022.
  20. Aiello, Francesco & Albanese, Giuseppe & Piselli, Paolo, 2019. "Good value for public money? The case of R&D policy," Journal of Policy Modeling, Elsevier, vol. 41(6), pages 1057-1076.
  21. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
  22. Hazal Colak Oz & Çiçek Güven & Gonzalo Nápoles, 2023. "School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach," Journal of Computational Social Science, Springer, vol. 6(1), pages 245-287, April.
  23. Müller, Stephan & Rau, Holger A., 2021. "Economic preferences and compliance in the social stress test of the COVID-19 crisis," Journal of Public Economics, Elsevier, vol. 194(C).
  24. Keyon Vafa & Susan Athey & David M. Blei, 2024. "Estimating Wage Disparities Using Foundation Models," Papers 2409.09894, arXiv.org.
  25. Emanuel Kohlscheen & Richhild Moessner, 2022. "Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix," Papers 2208.14650, arXiv.org.
  26. Jens Ludwig & Sendhil Mullainathan & Jann Spiess, 2017. "Machine-Learning Tests for Effects on Multiple Outcomes," Papers 1707.01473, arXiv.org, revised May 2019.
  27. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
  28. Resce, Giuliano, 2022. "The impact of political and non-political officials on the financial management of local governments," Journal of Policy Modeling, Elsevier, vol. 44(5), pages 943-962.
  29. Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022. "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, vol. 105(C).
  30. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
  31. Songul Tolan, 2018. "Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges," JRC Working Papers on Digital Economy 2018-10, Joint Research Centre.
  32. C'esar A. Hidalgo, 2022. "The Policy Implications of Economic Complexity," Papers 2205.02164, arXiv.org, revised Aug 2023.
  33. Oliver Lock & Michael Bain & Christopher Pettit, 2021. "Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example," Environment and Planning B, , vol. 48(3), pages 484-502, March.
  34. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
  35. Emanuel Kohlscheen, 2021. "What does machine learning say about the drivers of inflation?," BIS Working Papers 980, Bank for International Settlements.
  36. Krüger, Jens J. & Rhiel, Mathias, 2016. "Determinants of ICT infrastructure: A cross-country statistical analysis," Darmstadt Discussion Papers in Economics 228, Darmstadt University of Technology, Department of Law and Economics.
  37. Christian Posso & Jorge Tamayo & Arlen Guarin & Estefania Saravia, 2024. "Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes," Borradores de Economia 1269, Banco de la Republica de Colombia.
  38. Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
  39. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
  40. Vitezslav Titl & Deni Mazrekaj & Fritz Schiltz, 2024. "Identifying Politically Connected Firms: A Machine Learning Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 137-155, February.
  41. Ginevra Buratti & Alessio D'Ignazio, 2024. "Improving the effectiveness of financial education programs. A targeting approach," Journal of Consumer Affairs, Wiley Blackwell, vol. 58(2), pages 451-485, June.
  42. Momin M. Malik, 2020. "A Hierarchy of Limitations in Machine Learning," Papers 2002.05193, arXiv.org, revised Feb 2020.
  43. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
  44. Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
  45. Isabel Hovdahl, 2019. "On the use of machine learning for causal inference in climate economics," Working Papers No 05/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  46. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
  47. Pietro Battiston & Simona Gamba & Alessandro Santoro, 2020. "Optimizing Tax Administration Policies with Machine Learning," Working Papers 436, University of Milano-Bicocca, Department of Economics, revised Mar 2020.
  48. Alexei Alexandrov & Russell Pittman & Olga Ukhaneva, 2018. "Pricing of Complements in the U.S. Freight Railroads: Cournot Versus Coase," EAG Discussions Papers 201801, Department of Justice, Antitrust Division.
  49. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
  50. Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
  51. Emile Cammeraat & Brinn Hekkelman & Pim Kastelein & Suzanne Vissers, 2023. "Predictability and (co-)incidence of labor and health shocks," CPB Discussion Paper 453, CPB Netherlands Bureau for Economic Policy Analysis.
  52. Pauline Affeldt, 2019. "EU Merger Policy Predictability Using Random Forests," Discussion Papers of DIW Berlin 1800, DIW Berlin, German Institute for Economic Research.
  53. Erokhin, Dmitry & Zagler, Martin, 2024. "Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms," Economic Modelling, Elsevier, vol. 139(C).
  54. McKenzie, David & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.
  55. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2024. "Political favouritism and inefficient management: Policy-makers’ birth town bias in EU quality certifications," Journal of Policy Modeling, Elsevier, vol. 46(3), pages 683-702.
  56. Ashesh Rambachan & Jon Kleinberg & Sendhil Mullainathan & Jens Ludwig, 2020. "An Economic Approach to Regulating Algorithms," NBER Working Papers 27111, National Bureau of Economic Research, Inc.
  57. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
  58. Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
  59. Nikolaos Askitas, 2016. "Big Data is a big deal but how much data do we need? [Big Data gut und schön. Aber wie viel Data brauchen wir?]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 113-125, October.
  60. Steinkraus, Arne, 2018. "Rethinking Policy Evaluation – Do Simple Neural Nets Bear Comparison with Synthetic Control Method?," EconStor Preprints 177390, ZBW - Leibniz Information Centre for Economics.
  61. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
  62. Michael Allan Ribers & Hannes Ullrich, 2024. "Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing," Quantitative Marketing and Economics (QME), Springer, vol. 22(4), pages 445-483, December.
  63. Michael Allan Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers 1906.03044, arXiv.org.
  64. Zhou, Jinwei & Luo, Qi, 2024. "Influence factor studies based on ensemble learning on the innovation performance of technology mergers and acquisitions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 222(C), pages 67-89.
  65. Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
  66. Konstantin Boss & Finja Krueger & Conghan Zheng & Tobias Heidland & Andre Groeger, 2023. "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers 1387, Barcelona School of Economics.
  67. Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2017. "Royalty stacking in the U.S. freight railroads: Cournot vs. Coase," MPRA Paper 78249, University Library of Munich, Germany.
  68. DIODATO Dario, 2024. "Handbook of Economic Complexity for Policy," JRC Research Reports JRC138666, Joint Research Centre.
  69. Heidland, Tobias & Jannsen, Nils & Groll, Dominik & Kalweit, René & Boockmann, Bernhard, 2021. "Analyse und Prognose von Migrationsbewegungen," Kieler Beiträge zur Wirtschaftspolitik 34, Kiel Institute for the World Economy (IfW Kiel).
  70. Susan Athey & Herman Brunborg & Tianyu Du & Ayush Kanodia & Keyon Vafa, 2024. "LABOR-LLM: Language-Based Occupational Representations with Large Language Models," Papers 2406.17972, arXiv.org, revised Dec 2024.
  71. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
  72. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
  73. Keser, Claudia & Rau, Holger A., 2022. "Policy Incentives and Determinants of Citizens' COVID-19 Vaccination Motives," VfS Annual Conference 2022 (Basel): Big Data in Economics 264040, Verein für Socialpolitik / German Economic Association.
  74. Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
  75. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
  76. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
  77. Keser, Claudia & Rau, Holger A., 2022. "Policy incentives and determinants of citizens' COVID-19 vaccination motives," University of Göttingen Working Papers in Economics 434, University of Goettingen, Department of Economics.
  78. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
  79. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
  80. Tsun Se Cheong & Guanghua Wan & David Kam Hung Chui, 2022. "Unveiling the Relationship between Economic Growth and Equality for Developing Countries," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 30(5), pages 1-28, September.
  81. Bas Bosma & Arjen Witteloostuijn, 2024. "Machine learning in international business," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 55(6), pages 676-702, August.
  82. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  83. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
  84. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  85. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
  86. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
  87. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  88. Giri, Prashant & Sharma, Tarun, 2024. "Market instrument for the first fuel and its role in decarbonizing Indian industrial production," Energy Policy, Elsevier, vol. 190(C).
  89. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
  90. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
  91. Jorge Mejia & Shawn Mankad & Anandasivam Gopal, 2019. "A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections," Information Systems Research, INFORMS, vol. 30(4), pages 1363-1386, December.
  92. Zhu, Jingjing & Huang, Tianyuan, 2024. "Public debt and welfare with machine learning," Finance Research Letters, Elsevier, vol. 69(PA).
  93. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2021. "Local mortality estimates during the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1189-1217, October.
  94. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
  95. Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.
  96. Brown, David P. & Cajueiro, Daniel O. & Eckert, Andrew & Silveira, Douglas, 2024. "Evaluating the Role of Information Disclosure on Bidding Behavior in Wholesale Electricity Markets," Working Papers 2024-2, University of Alberta, Department of Economics.
  97. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
  98. Jongbin Jung & Connor Concannon & Ravi Shroff & Sharad Goel & Daniel G. Goldstein, 2020. "Simple rules to guide expert classifications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 771-800, June.
  99. Rama K. Malladi, 2024. "Benchmark Analysis of Machine Learning Methods to Forecast the U.S. Annual Inflation Rate During a High-Decile Inflation Period," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 335-375, July.
  100. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
  101. Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Kraay,Aart C. & Spencer,Phoebe Girouard & Wang,Dieter, 2020. "Predicting Food Crises," Policy Research Working Paper Series 9412, The World Bank.
  102. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
  103. Resce, Giuliano, 2022. "Political and Non-Political Officials in Local Government," Economics & Statistics Discussion Papers esdp22079, University of Molise, Department of Economics.
  104. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
  105. Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3226-3264, National Bureau of Economic Research, Inc.
  106. Sabahi, Sima & Parast, Mahour Mellat, 2020. "The impact of entrepreneurship orientation on project performance: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 226(C).
  107. Coco, Giuseppe & Monturano, Gianluca & Resce, Giuliano, 2025. "Predicting Delays in Cohesion Infrastructure Projects," Economics & Statistics Discussion Papers esdp25099, University of Molise, Department of Economics.
  108. Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
  109. Sendhil Mullainathan & Ziad Obermeyer, 2023. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(2), pages 679-727.
  110. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
  111. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
  112. Xie, Wen-Jie & Wei, Na & Zhou, Wei-Xing, 2023. "An interpretable machine-learned model for international oil trade network," Resources Policy, Elsevier, vol. 82(C).
  113. Prothit Sen & Phanish Puranam, 2022. "Do Alliance portfolios encourage or impede new business practice adoption? Theory and evidence from the private equity industry," Strategic Management Journal, Wiley Blackwell, vol. 43(11), pages 2279-2312, November.
  114. Madhura Dasgupta & Samarth Gupta, 2024. "What Determines Enterprise Borrowing from Self Help Groups? An Interpretable Supervised Machine Learning Approach," Journal of Financial Services Research, Springer;Western Finance Association, vol. 66(1), pages 77-99, August.
  115. Luca Panzone & Guy Garrod & Felice Adinolfi & Jorgelina Di Pasquale, 2022. "Molecular marketing, personalised information and willingness‐to‐pay for functional foods: Vitamin D enriched eggs," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(3), pages 666-689, September.
  116. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
  117. Ballestar, María Teresa & Doncel, Luis Miguel & Sainz, Jorge & Ortigosa-Blanch, Arturo, 2019. "A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
  118. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
  119. Jianghong Xu & Chenguang Wang & Xukang Yin & Weixin Wang, 2024. "Digital economy and rural household resilience: Evidence from China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(5), pages 244-263.
  120. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
  121. Potnuru Kishen Suraj & Ankesh Gupta & Makkunda Sharma & Sourabh Bikas Paul & Subhashis Banerjee, 2017. "On monitoring development indicators using high resolution satellite images," Papers 1712.02282, arXiv.org, revised Jun 2018.
  122. S. Mills & S. Costa & C. R. Sunstein, 2023. "AI, Behavioural Science, and Consumer Welfare," Journal of Consumer Policy, Springer, vol. 46(3), pages 387-400, September.
  123. Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.
  124. Huang, Shan & Ribers, Michael Allan & Ullrich, Hannes, 2022. "Assessing the value of data for prediction policies: The case of antibiotic prescribing," Economics Letters, Elsevier, vol. 213(C).
  125. Hannes Ullrich & Michael Allan Ribers, 2023. "Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing," Berlin School of Economics Discussion Papers 0027, Berlin School of Economics.
  126. Md Saiful Islam & Md Sarowar Morshed & Gary J Young & Md Noor-E-Alam, 2019. "Robust policy evaluation from large-scale observational studies," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
  127. Zhiqiang Xu & Mahdi Aghaabbasi & Mujahid Ali & Elżbieta Macioszek, 2022. "Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
  128. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
  129. Potash, Eric, 2018. "Randomization bias in field trials to evaluate targeting methods," Economics Letters, Elsevier, vol. 167(C), pages 131-135.
  130. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
  131. Heller, Yuval & Tubul, Itay, 2023. "Strategies in the repeated prisoner’s dilemma: A cluster analysis," MPRA Paper 117444, University Library of Munich, Germany.
  132. Boubacar Diallo, 2022. "Machine learning approaches to testing institutional hypotheses: the case of Acemoglu, Johnson, and Robinson (2001)," Empirical Economics, Springer, vol. 62(5), pages 2587-2600, May.
  133. Christian Terwiesch, 2019. "OM Forum—Empirical Research in Operations Management: From Field Studies to Analyzing Digital Exhaust," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 713-722, October.
  134. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
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