The Impact of Machine Learning on Economics
In: The Economics of Artificial Intelligence: An Agenda
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- Eckles Dean & Karrer Brian & Ugander Johan, 2017. "Design and Analysis of Experiments in Networks: Reducing Bias from Interference," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-23, March.
- Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2016.
"Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy,"
American Economic Review, American Economic Association, vol. 106(5), pages 114-118, May.
- Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2016. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy," NBER Working Papers 22124, National Bureau of Economic Research, Inc.
- 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.
- Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2015. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life," NBER Working Papers 21778, National Bureau of Economic Research, Inc.
- Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2015. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life," Harvard Business School Working Papers 16-065, Harvard Business School.
- Glaeser, Edward L. & Kominers, Scott Duke & Luca, Michael & Naik, Nikhil, 2015. "Big Data and Big Cities: The Promises and Limitations of Improved Measures for Urban Life," Working Paper Series 15-075, Harvard University, John F. Kennedy School of Government.
- Toru Kitagawa & Aleksey Tetenov, 2018.
"Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice,"
Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
- Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers CWP10/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Toru Kitagawa & Aleksey Tetenov, 2017. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers CWP24/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Carlo Alberto Notebooks 402, Collegio Carlo Alberto.
- Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013.
"Now-Casting and the Real-Time Data Flow,"
Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237,
Elsevier.
- Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
- Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta & Modugno, Michele, 2013. "Now-casting and the real-time data flow," Working Paper Series 1564, European Central Bank.
- Martha Banbura & Domenico Giannone & Michèle Modugno & Lucrezia Reichlin, 2012. "Now-Casting and the Real-Time Data Flow," Working Papers ECARES ECARES 2012-026, ULB -- Universite Libre de Bruxelles.
- 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.
- Wager, Stefan & Athey, Susan, 2017. "Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests," Research Papers 3576, Stanford University, Graduate School of Business.
- Susan Athey & Dean Eckles & Guido W. Imbens, 2018.
"Exact p-Values for Network Interference,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 230-240, January.
- Athey, Susan & Eckles, Dean & Imbens, Guido W., 2015. "Exact P-Values for Network Interference," Research Papers 3351, Stanford University, Graduate School of Business.
- Susan Athey & Dean Eckles & Guido W. Imbens, 2015. "Exact P-values for Network Interference," NBER Working Papers 21313, National Bureau of Economic Research, Inc.
- Athey, Susan & Eckles, Dean & Imbens, Guido W., 2015. "Exact P-Values for Network Interference," Research Papers 3287, Stanford University, Graduate School of Business.
- Robert Donnelly & Francisco J.R. Ruiz & David Blei & Susan Athey, 2021.
"Counterfactual inference for consumer choice across many product categories,"
Quantitative Marketing and Economics (QME), Springer, vol. 19(3), pages 369-407, December.
- Rob Donnelly & Francisco R. Ruiz & David Blei & Susan Athey, 2019. "Counterfactual Inference for Consumer Choice Across Many Product Categories," Papers 1906.02635, arXiv.org, revised Aug 2023.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014.
"High-Dimensional Methods and Inference on Structural and Treatment Effects,"
Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.
- 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.
- Susan Athey & Guido Imbens, 2016. "The State of Applied Econometrics - Causality and Policy Evaluation," Papers 1607.00699, arXiv.org.
- Daniel Björkegren & Darrell Grissen, 0.
"Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment,"
The World Bank Economic Review, World Bank, vol. 34(3), pages 618-634.
- Bjorkegren,Daniel & Grissen,Darrell, 2019. "Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment," Policy Research Working Paper Series 9074, The World Bank.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
- van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015.
"Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach,"
Annual Review of Economics, Annual Reviews, vol. 7(1), pages 649-688, August.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Papers 1501.03430, arXiv.org, revised Aug 2015.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers 36/16, Institute for Fiscal Studies.
- Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Sam Asher & Denis Nekipelov & Paul Novosad & Stephen P. Ryan, 2016. "Classification Trees for Heterogeneous Moment-Based Models," NBER Working Papers 22976, National Bureau of Economic Research, Inc.
- Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
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