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Human Decisions and Machine Predictions
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Cited by:
- Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020.
"Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice,"
CEPR Discussion Papers
15418, C.E.P.R. Discussion Papers.
- Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," Papers 2010.08463, arXiv.org, revised Nov 2021.
- 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.
- MIYAKAWA Daisuke, 2019. "Shocks to Supply Chain Networks and Firm Dynamics: An Application of Double Machine Learning," Discussion papers 19100, Research Institute of Economy, Trade and Industry (RIETI).
- Richard Berk, 2019. "Accuracy and Fairness for Juvenile Justice Risk Assessments," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 16(1), pages 175-194, March.
- Achten, Sandra & Lessmann, Christian, 2020.
"Spatial inequality, geography and economic activity,"
World Development, Elsevier, vol. 136(C).
- Sandra Achten & Christian Lessmann, 2019. "Spatial inequality, geography and economic activity," CESifo Working Paper Series 7547, CESifo.
- Peter Leopold S. Bergman & Elizabeth Kopko & Julio Rodriguez, 2021.
"Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment,"
CESifo Working Paper Series
9157, CESifo.
- Bergman, Peter & Kopko, Elizabeth & Rodriguez, Julio, 2021. "Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment," IZA Discussion Papers 14500, Institute of Labor Economics (IZA).
- Bauer, Kevin & Gill, Andrej, 2021. "Mirror, mirror on the wall: Machine predictions and self-fulfilling prophecies," SAFE Working Paper Series 313, Leibniz Institute for Financial Research SAFE.
- 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).
- 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.
- Ivan A. Canay & Magne Mogstad & Jack Mountjoy, 2020.
"On the Use of Outcome Tests for Detecting Bias in Decision Making,"
NBER Working Papers
27802, National Bureau of Economic Research, Inc.
- Ivan A. Canay & Magne Mogstad & Jack Mountjoy, 2020. "On the Use of Outcome Tests for Detecting Bias in Decision Making," Working Papers 2020-125, Becker Friedman Institute for Research In Economics.
- Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019.
"Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction,"
Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
- Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," NBER Working Papers 25619, National Bureau of Economic Research, Inc.
- Maria De‐Arteaga & Stefan Feuerriegel & Maytal Saar‐Tsechansky, 2022. "Algorithmic fairness in business analytics: Directions for research and practice," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3749-3770, October.
- Valerio Capraro & Roberto Di Paolo & Veronica Pizziol, 2023. "Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others," Papers 2307.12776, arXiv.org, revised Feb 2024.
- Gambardella, Alfonso, 2023. "Private and social functions of patents: Innovation, markets, and new firms," Research Policy, Elsevier, vol. 52(7).
- Laura Toschi & Elisa Ughetto & Andrea Fronzetti Colladon, 2023. "The identity of social impact venture capitalists: exploring social linguistic positioning and linguistic distinctiveness through text mining," Small Business Economics, Springer, vol. 60(3), pages 1249-1280, March.
- 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.
- Yoan Hermstrüwer & Pascal Langenbach, 2022. "Fair Governance with Humans and Machines," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2022_04, Max Planck Institute for Research on Collective Goods, revised 01 Mar 2023.
- Kristian Lum & David B. Dunson & James Johndrow, 2022. "Closer than they appear: A Bayesian perspective on individual‐level heterogeneity in risk assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 588-614, April.
- Shroff, Ravi & Vamvourellis, Konstantinos, 2022. "Pretrial release judgments and decision fatigue," LSE Research Online Documents on Economics 117579, London School of Economics and Political Science, LSE Library.
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2020.
"lassopack: Model selection and prediction with regularized regression in Stata,"
Stata Journal, StataCorp LP, vol. 20(1), pages 176-235, March.
- Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E, 2019. "lassopack: Model Selection and Prediction with Regularized Regression in Stata," IZA Discussion Papers 12081, Institute of Labor Economics (IZA).
- Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
- 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.
- Gallego, J & Rivero, G & Martínez, J.D., 2018. "Preventing rather than Punishing: An Early Warning Model of Malfeasance in Public Procurement," Documentos de Trabajo 16724, Universidad del Rosario.
- Leib, Margarita & Köbis, Nils & Rilke, Rainer Michael & Hagens, Marloes & Irlenbusch, Bernd, 2023. "Corrupted by Algorithms? How AI-Generated and Human-Written Advice Shape (Dis)Honesty," IZA Discussion Papers 16293, Institute of Labor Economics (IZA).
- Daniela Sele & Marina Chugunova, 2023. "Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making," Rationality and Competition Discussion Paper Series 438, CRC TRR 190 Rationality and Competition.
- Susan Athey & Stefan Wager, 2021.
"Policy Learning With Observational Data,"
Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
- Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
- Beth Coulthard & John Mallett & Brian Taylor, 2020. "Better Decisions for Children with “Big Data”: Can Algorithms Promote Fairness, Transparency and Parental Engagement?," Societies, MDPI, vol. 10(4), pages 1-16, December.
- Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
- Jeffrey Penney & Steven Lehrer & Emilia Galan, 2024. "Mandatory minimum sentencing and its effect on sentencing distributions: Evidence from Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 57(1), pages 55-77, February.
- Jean-Marie John-Mathews & Dominique Cardon & Christine Balagué, 2022. "From Reality to World. A Critical Perspective on AI Fairness," Journal of Business Ethics, Springer, vol. 178(4), pages 945-959, July.
- Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2021.
"Measuring Bias in Consumer Lending [Loan Prospecting and the Loss of Soft Information],"
Review of Economic Studies, Oxford University Press, vol. 88(6), pages 2799-2832.
- Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2018. "Measuring Bias in Consumer Lending," NBER Working Papers 24953, National Bureau of Economic Research, Inc.
- Will Dobbie & Andres Liberman & Daniel Paravisini & Vikram Pathania, 2018. "Measuring Bias in Consumer Lending," Working Papers 623, Princeton University, Department of Economics, Industrial Relations Section..
- Dobbie, Will & Liberman, Andres & Paravisini, Daniel & Pathania, Vikram S., 2021. "Measuring bias in consumer lending," LSE Research Online Documents on Economics 104984, London School of Economics and Political Science, LSE Library.
- Dobbie, Will & Liberman, Andres & Paravisini, Daniel & Pathania, Vikram, 2019. "Measuring Bias in Consumer Lending," Working Paper Series rwp19-029, Harvard University, John F. Kennedy School of Government.
- Nathalie de Marcellis-Warin & Frédéric Marty & Eva Thelisson & Thierry Warin, 2020.
"Artificial Intelligence and Market Manipulations: Ex-ante Evaluation in the Regulator's Arsenal,"
CIRANO Working Papers
2020s-64, CIRANO.
- Nathalie de Marcellis-Warin & Frédéric Marty & Eva Thelisson & Thierry Warin, 2020. "Artificial Intelligence and Market Manipulations: Ex-ante Evaluation in the Regulator's Arsenal," Working Papers halshs-03041690, HAL.
- Klockmann, Victor & von Schenk, Alicia & Villeval, Marie Claire, 2022.
"Artificial intelligence, ethics, and intergenerational responsibility,"
Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 284-317.
- Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2021. "Artificial Intelligence, Ethics, and Intergenerational Responsibility," Working Papers halshs-03237437, HAL.
- Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2022. "Artificial Intelligence, Ethics, and Intergenerational Responsibility," Post-Print hal-03778525, HAL.
- Victor Klockmann & Alicia von Schenk & Marie Claire Villeval, 2021. "Artificial Intelligence, Ethics, and Intergenerational Responsibility," Working Papers 2110, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
- Klockmann, Victor & von Schenk, Alicia & Villeval, Marie-Claire, 2022. "Artificial intelligence, ethics, and intergenerational responsibility," SAFE Working Paper Series 335, Leibniz Institute for Financial Research SAFE.
- Runshan Fu & Ginger Zhe Jin & Meng Liu, 2022. "Does Human-algorithm Feedback Loop Lead to Error Propagation? Evidence from Zillow’s Zestimate," NBER Working Papers 29880, National Bureau of Economic Research, Inc.
- Roshni Sahoo & Stefan Wager, 2022. "Policy Learning with Competing Agents," Papers 2204.01884, arXiv.org, revised Dec 2023.
- Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.
- Michael Allan Ribers & Hannes Ullrich, 2020.
"Machine Predictions and Human Decisions with Variation in Payoffs and Skill,"
CESifo Working Paper Series
8702, CESifo.
- Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," Papers 2011.11017, arXiv.org.
- Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skills," Discussion Papers of DIW Berlin 1911, DIW Berlin, German Institute for Economic Research.
- Jella Pfeiffer & Julia Gutschow & Christian Haas & Florian Möslein & Oliver Maspfuhl & Frederik Borgers & Suzana Alpsancar, 2023. "Algorithmic Fairness in AI," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 209-222, April.
- Michael Allan Ribers & Hannes Ullrich, 2019.
"Battling antibiotic resistance: can machine learning improve prescribing?,"
CESifo Working Paper Series
7654, CESifo.
- Michael A. Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Discussion Papers of DIW Berlin 1803, DIW Berlin, German Institute for Economic Research.
- Michael Allan Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers 1906.03044, arXiv.org.
- Adam Altmejd & Anna Dreber & Eskil Forsell & Juergen Huber & Taisuke Imai & Magnus Johannesson & Michael Kirchler & Gideon Nave & Colin Camerer, 2019.
"Predicting the replicability of social science lab experiments,"
PLOS ONE, Public Library of Science, vol. 14(12), pages 1-18, December.
- Altmejd, Adam & Dreber, Anna & Forsell, Eskil & Huber, Jürgen & Imai, Taisuke & Johannesson, Magnus & Kirchler, Michael & Nave, Gideon & Camerer, Colin, 2019. "Predicting the replicability of social science lab experiments," Munich Reprints in Economics 78212, University of Munich, Department of Economics.
- Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022.
"Artificial intelligence and systemic risk,"
Journal of Banking & Finance, Elsevier, vol. 140(C).
- Danielsson, Jon & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," LSE Research Online Documents on Economics 111601, London School of Economics and Political Science, LSE Library.
- 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.
- Tamer Boyaci, & Caner Canyakmaz, & Francis de Véricourt,, 2020. "Human and machine: The impact of machine input on decision-making under cognitive limitations," ESMT Research Working Papers ESMT-20-02, ESMT European School of Management and Technology.
- Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
- Brendan O'Flaherty & Rajiv Sethi & Morgan Williams, 2024. "The nature, detection, and avoidance of harmful discrimination in criminal justice," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 43(1), pages 289-320, January.
- Daniel Carter & Amelia Acker & Dan Sholler, 2021. "Investigative approaches to researching information technology companies," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(6), pages 655-666, June.
- Margarita Leib & Nils Kobis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2023. "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty," Papers 2301.01954, arXiv.org.
- Anja Lambrecht & Catherine Tucker, 2019. "Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads," Management Science, INFORMS, vol. 65(7), pages 2966-2981, July.
- Robert John Zagar & James Garbarino & Brad Randmark & Ishup Singh & Joseph Kovach & Emma Cenzon & Michael Benko & Steve Tippins & Kenneth G. Busch, 2022. "Study 1: 630 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ from 623 Controls and Study 2: 15 Domestic-Terrorist, Mass-Murdering, Spree-Shooters Differ From 23 Homicidal and 36 Controls on ," Review of European Studies, Canadian Center of Science and Education, vol. 14(1), pages 1-54, March.
- 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.
- Alexander Erlei & Lukas Meub, 2024. "Technological Shocks and Algorithmic Decision Aids in Credence Goods Markets," Papers 2401.17929, arXiv.org.
- Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
- Margarita Leib & Nils Köbis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2023. "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty," ECONtribute Discussion Papers Series 251, University of Bonn and University of Cologne, Germany.
- Escobar, Maria A. & Tobón, Santiago & Vanegas-Arias, Martín, 2023. "Production and persistence of criminal skills: Evidence from a high-crime context," Journal of Development Economics, Elsevier, vol. 160(C).
- 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).
- Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2021. "Towards Data-driven Project design: Providing Optimal Treatment Rules for Development Projects," 2021 Annual Meeting, August 1-3, Austin, Texas 314016, Agricultural and Applied Economics Association.
- 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.
- Dionissi Aliprantis & Kristen Tauber & Hal Martin, 2022. "What Determines the Success of Housing Mobility Programs?," Working Papers 2022-043, Human Capital and Economic Opportunity Working Group.
- D'Acunto, Francesco & Ghosh, Pulak & Jain, Rajiv & Rossi, Alberto G., 2022. "How costly are cultural biases?," LawFin Working Paper Series 34, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
- Chen, S. & Doerr, S. & Frost, J. & Gambacorta, L. & Shin, H.S., 2023.
"The fintech gender gap,"
Journal of Financial Intermediation, Elsevier, vol. 54(C).
- Sharon Chen & Sebastian Doerr & Jon Frost & Leonardo Gambacorta & Hyun Song Shin, 2021. "The fintech gender gap," BIS Working Papers 931, Bank for International Settlements.
- Gambacorta, Leonardo & Chen, Sharon & Doerr, Sebastian & Frost, Jon & Shin, Hyun Song, 2021. "The fintech gender gap," CEPR Discussion Papers 16270, C.E.P.R. Discussion Papers.
- Maude Lavanchy & Patrick Reichert & Jayanth Narayanan & Krishna Savani, 2023. "Applicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures," Journal of Business Ethics, Springer, vol. 188(1), pages 125-150, November.
- Khoa Hoang & Robert Faff, 2021. "Is the ex‐ante equity risk premium always positive? Evidence from a new conditional expectations model," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(1), pages 95-124, March.
- Jon Kleinberg & Sendhil Mullainathan, 2019. "Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability," NBER Working Papers 25854, National Bureau of Economic Research, Inc.
- Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024.
"Predicting dropout from higher education: Evidence from Italy,"
Economic Modelling, Elsevier, vol. 130(C).
- Marco Delogu & Raffaelle Lagravinese & Dimitri Paolini & Giuliano Resce, 2022. "Predicting dropout from higher education: Evidence from Italy," DEM Discussion Paper Series 22-06, Department of Economics at the University of Luxembourg.
- Alex James & Jeanette McLeod & Shaun Hendy & Kip Marks & Delia Rusu & Syen Nik & Michael J Plank, 2019. "Using family network data in child protection services," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-14, October.
- Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
- Sendhil Mullainathan & Ziad Obermeyer, 2019. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care," NBER Working Papers 26168, National Bureau of Economic Research, Inc.
- Ratzanyel Rincón, 2023. "Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms/Estimaciones trimestrales de pobreza multidimensional en México mediante algoritmos de aprendizaje de máquina," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 38(1), pages 3-68.
- 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.
- Charles Hoffreumon & Chris CM Forman & Nicolas van Zeebroeck, 2023. "Make or Buy your Artificial Intelligence? Complementarities in Technology Sourcing," Working Papers TIMES² 2023-69, ULB -- Universite Libre de Bruxelles.
- Yael Karlinsky-Shichor & Oded Netzer, 2024. "Automating the B2B Salesperson Pricing Decisions: A Human-Machine Hybrid Approach," Marketing Science, INFORMS, vol. 43(1), pages 138-157, January.
- 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.
- Huijian Han & Zhiming Li & Zongwei Li, 2023. "Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data," Sustainability, MDPI, vol. 15(4), pages 1-12, February.
- Said Kaawach & Oskar Kowalewski & Oleksandr Talavera, 2023. "Automatic vs Manual Investing: Role of Past Performance," Discussion Papers 23-04, Department of Economics, University of Birmingham.
- Dargnies, Marie-Pierre & Hakimov, Rustamdjan & Kübler, Dorothea, 2022.
"Aversion to hiring algorithms: Transparency, gender profiling, and self-confidence,"
Discussion Papers, Research Unit: Market Behavior
SP II 2022-202, WZB Berlin Social Science Center.
- Marie-Pierre Dargnies & Rustamdjan Hakimov & Dorothea Kübler, 2022. "Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence," CESifo Working Paper Series 9968, CESifo.
- Dargnies, Marie-Pierre & Hakimov, Rustamdjan & Kübler, Dorothea, 2022. "Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence," Rationality and Competition Discussion Paper Series 334, CRC TRR 190 Rationality and Competition.
- Ian Lundberg & Arvind Narayanan & Karen Levy & Matthew Salganik, 2018. "Privacy, ethics, and data access: A case study of the Fragile Families Challenge," Working Papers wp18-09-ff, Princeton University, School of Public and International Affairs, Center for Research on Child Wellbeing..
- 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.
- Vitezslav Titl & Fritz Schiltz, 2021. "Identifying Politically Connected Firms: A Machine Learning Approach," Working Papers 2110, Utrecht School of Economics.
- Prithwiraj Choudhury & Evan Starr & Rajshree Agarwal, 2020. "Machine learning and human capital complementarities: Experimental evidence on bias mitigation," Strategic Management Journal, Wiley Blackwell, vol. 41(8), pages 1381-1411, August.
- Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
- Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
- Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
- 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.
- Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021. "Selecting Directors Using Machine Learning [The role of boards of directors in corporate governance: A conceptual framework and survey]," Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3226-3264.
- Erel, Isil & Stern, Lea Henny & Tan, Chenhao & Weisbach, Michael S., 2018. "Selecting Directors Using Machine Learning," Working Paper Series 2018-05, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
- Isil Erel & Léa H. Stern & Chenhao Tan & Michael S. Weisbach, 2018. "Selecting Directors Using Machine Learning," NBER Working Papers 24435, National Bureau of Economic Research, Inc.
- Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2019. "Measuring the Completeness of Theories," Papers 1910.07022, arXiv.org.
- Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
- Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
- Juan Carlos Perdomo, 2023. "The Relative Value of Prediction in Algorithmic Decision Making," Papers 2312.08511, arXiv.org.
- Ali Bakdur & Fumito Masui & Michal Ptaszynski, 2021. "Predicting Increase in Demand for Public Buses in University Students Daily Life Needs: Case Study Based on a City in Japan," Sustainability, MDPI, vol. 13(9), pages 1-28, May.
- Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
- Yusuke Narita & Kohei Yata, 2021.
"Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules,"
Working Papers
2021-022, Human Capital and Economic Opportunity Working Group.
- Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Cowles Foundation Discussion Papers 2283, Cowles Foundation for Research in Economics, Yale University.
- Yusuke Narita & Kohei Yata, 2021. "Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Papers 2104.12909, arXiv.org, revised Dec 2023.
- NARITA Yusuke & YATA Kohei, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Discussion papers 21057, Research Institute of Economy, Trade and Industry (RIETI).
- 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.
- Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
- Jing Wu & Zhaocheng Zhang & Sean X. Zhou, 2022. "Credit Rating Prediction Through Supply Chains: A Machine Learning Approach," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1613-1629, April.
- Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2020. "Why are some Chinese firms failing in the US capital markets? A machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
- Manav Raj & Robert Seamans, 2019. "Primer on artificial intelligence and robotics," Journal of Organization Design, Springer;Organizational Design Community, vol. 8(1), pages 1-14, December.
- David Almog & Romain Gauriot & Lionel Page & Daniel Martin, 2024. "AI Oversight and Human Mistakes: Evidence from Centre Court," Papers 2401.16754, arXiv.org, revised Feb 2024.
- Bharti, Nitin Kumar & Roy, Sutanuka, 2023. "The early origins of judicial stringency in bail decisions: Evidence from early childhood exposure to Hindu-Muslim riots in India," Journal of Public Economics, Elsevier, vol. 221(C).
- Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
- Ola G. El‐Taliawi & Nihit Goyal & Michael Howlett, 2021. "Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching," Review of Policy Research, Policy Studies Organization, vol. 38(6), pages 640-660, November.
- Daniel Bjorkegren & Joshua E. Blumenstock & Samsun Knight, 2020. "Manipulation-Proof Machine Learning," Papers 2004.03865, arXiv.org.
- Christoph Engel, 2018.
"Empirical Methods for the Law,"
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