Tree-based Synthetic Control Methods: Consequences of moving the US Embassy
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- 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.
- 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.
- Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021.
"An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls,"
Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
- Victor Chernozhukov & Kaspar Wüthrich & Yu Zhu, 2017. "An exact and robust conformal inference method for counterfactual and synthetic controls," CeMMAP working papers 62/17, Institute for Fiscal Studies.
- Chernozhukov, Victor & Wüthrich, Kaspar & Zhu, Yinchu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," University of California at San Diego, Economics Working Paper Series qt90m9d66s, Department of Economics, UC San Diego.
- Victor Chernozhukov & Kaspar Wüthrich & Yu Zhu, 2017. "An exact and robust conformal inference method for counterfactual and synthetic controls," CeMMAP working papers CWP62/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2017. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Papers 1712.09089, arXiv.org, revised May 2021.
- Rosenbaum, Paul R., 2007. "Interference Between Units in Randomized Experiments," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 191-200, March.
- Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "A $t$-test for synthetic controls," Papers 1812.10820, arXiv.org, revised Jan 2024.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
- Momin M. Malik, 2020. "A Hierarchy of Limitations in Machine Learning," Papers 2002.05193, arXiv.org, revised Feb 2020.
- 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.
- Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org.
- Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
- 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.
- Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023.
"What’s trending in difference-in-differences? A synthesis of the recent econometrics literature,"
Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
- Jonathan Roth & Pedro H. C. Sant'Anna & Alyssa Bilinski & John Poe, 2022. "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature," Papers 2201.01194, arXiv.org, revised Jan 2023.
- 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.
- Zhaonan Qu & Ruoxuan Xiong & Jizhou Liu & Guido Imbens, 2021. "Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference," Papers 2107.12420, arXiv.org, revised Jun 2024.
- Bruno Ferman & Cristine Pinto, 2021.
"Synthetic controls with imperfect pretreatment fit,"
Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
- Bruno Ferman & Cristine Pinto, 2019. "Synthetic Controls with Imperfect Pre-Treatment Fit," Papers 1911.08521, arXiv.org, revised Jan 2021.
- Yan Zhang & Zudi Lu, 2024. "A Time Series Synthetic Control Causal Evaluation of the UK’s Mini-Budget Policy on Stock Market," Mathematics, MDPI, vol. 12(20), pages 1-25, October.
- 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.
- Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
- Cerqua, Augusto & Letta, Marco, 2022.
"Local inequalities of the COVID-19 crisis,"
Regional Science and Urban Economics, Elsevier, vol. 92(C).
- Cerqua, Augusto & Letta, Marco, 2021. "Local inequalities of the COVID-19 crisis," GLO Discussion Paper Series 875, Global Labor Organization (GLO).
- 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.
- 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.
- Vivek F. Farias & Andrew A. Li & Tianyi Peng, 2021. "Learning Treatment Effects in Panels with General Intervention Patterns," Papers 2106.02780, arXiv.org, revised Mar 2023.
- 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.
- Cerqua, Augusto & Letta, Marco, 2020. "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper 104404, University Library of Munich, Germany.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2019-09-16 (Big Data)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1909.03968. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.