Aaron R Kaufman

Personal Details

First Name:	Aaron
Middle Name:	Russell
Last Name:	Kaufman
Suffix:
RePEc Short-ID:	pka1569
	[This author has chosen not to make the email address public]
	http://www.aaronrkaufman.com

Affiliation

(1%) Institute for Quantitative Social Science
Harvard University

Cambridge, Massachusetts (United States)
http://iq.harvard.edu/
RePEc:edi:cbrssus (more details at EDIRC)

(99%) Economics
New York University Abu Dhabi

Abu Dhabi, United Arab Emirates
https://nyuad.nyu.edu/en/academics/divisions/social-science.html
RePEc:edi:ecnyuae (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

Sorry, no citations of working papers recorded.

Articles

1. Mozer, Reagan & Miratrix, Luke & Kaufman, Aaron Russell & Jason Anastasopoulos, L., 2020. "Matching with Text Data: An Experimental Evaluation of Methods for Matching Documents and of Measuring Match Quality," Political Analysis, Cambridge University Press, vol. 28(4), pages 445-468, October.

   Cited by:

   1. Hao Chen & Dylan S. Small, 2022. "New multivariate tests for assessing covariate balance in matched observational studies," Biometrics, The International Biometric Society, vol. 78(1), pages 202-213, March.
   2. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
   3. Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
   4. Zeng, Jiaming & Gensheimer, Michael F. & Rubin, Daniel L. & Athey, Susan & Schachter, Ross D., 2021. "Uncovering Interpretable Potential Confounders in Electronic Medical Records," Research Papers 3950, Stanford University, Graduate School of Business.
      • Jiaming Zeng & Michael F. Gensheimer & Daniel L. Rubin & Susan Athey & Ross D. Shachter, 2022. "Uncovering interpretable potential confounders in electronic medical records," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
   5. Henrika Langen, 2022. "The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach," Papers 2209.00409, arXiv.org, revised Sep 2023.
   6. Roman Senninger & Jens Blom‐Hansen, 2021. "Meet the critics: Analyzing the EU Commission's Regulatory Scrutiny Board through quantitative text analysis," Regulation & Governance, John Wiley & Sons, vol. 15(4), pages 1436-1453, October.
   7. Margaret E. Roberts & Brandon M. Stewart & Richard A. Nielsen, 2020. "Adjusting for Confounding with Text Matching," American Journal of Political Science, John Wiley & Sons, vol. 64(4), pages 887-903, October.

2. Aaron R Kaufman & Eitan D Hersh, 2020. "The political consequences of opioid overdoses," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-10, August.

   Cited by:

   1. Testa, Alexander & Lee, Jacqueline, 2021. "Drug overdose death rates and criminal sentencing of federal drug offenders in the United States," Journal of Criminal Justice, Elsevier, vol. 74(C).
   2. Gollust, Sarah E. & Haselswerdt, Jake, 2021. "A crisis in my community? Local-level awareness of the opioid epidemic and political consequences," Social Science & Medicine, Elsevier, vol. 291(C).

3. Enos, Ryan D. & Kaufman, Aaron R. & Sands, Melissa L., 2019. "Can Violent Protest Change Local Policy Support? Evidence from the Aftermath of the 1992 Los Angeles Riot," American Political Science Review, Cambridge University Press, vol. 113(4), pages 1012-1028, November.

   Cited by:

   1. Felipe González & Magdalena Larreboure, 2021. "The Impact of the Women’s March on the U.S. House Election," Documentos de Trabajo 560, Instituto de Economia. Pontificia Universidad Católica de Chile..
   2. Aaron R Kaufman & Eitan D Hersh, 2020. "The political consequences of opioid overdoses," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-10, August.
   3. Stephen B. Billings & Eric Chyn & Kareem Haggag, 2020. "The Long-Run Effects of School Racial Diversity on Political Identity," NBER Working Papers 27302, National Bureau of Economic Research, Inc.
      • Stephen B. Billings & Eric Chyn & Kareem Haggag, 2021. "The Long-Run Effects of School Racial Diversity on Political Identity," American Economic Review: Insights, American Economic Association, vol. 3(3), pages 267-284, September.
   4. Justin Curtis, 2022. "The effect of the 2020 racial justice protests on attitudes and preferences in rural and urban America," Social Science Quarterly, Southwestern Social Science Association, vol. 103(1), pages 90-107, January.
   5. Felipe González, 2018. "Collective Action in Networks: Evidence from the Chilean Student Movement," Documentos de Trabajo 509, Instituto de Economia. Pontificia Universidad Católica de Chile..
      • González, Felipe, 2020. "Collective action in networks: Evidence from the Chilean student movement," Journal of Public Economics, Elsevier, vol. 188(C).
   6. Nupia Martínez, Oscar & Álvarez Gallo, Carlos Andrés, 2024. "The Impact of Massive Protests on Individual Attitudes," Documentos CEDE 21190, Universidad de los Andes, Facultad de Economía, CEDE.
   7. Freitas Monteiro, Teresa & Prömel, Christopher, 2024. "Local far-right demonstrations and nationwide public attitudes toward migration," Discussion Papers 2024/3, Free University Berlin, School of Business & Economics.
   8. Bouke Klein Teeselink & Georgios Melios, 2022. "Weather to Protest: The Effect of Black Lives Matter Protests on the 2020 Presidential Election," Working Papers CEB 22-007, ULB -- Universite Libre de Bruxelles.

4. Kaufman, Aaron Russell & Kraft, Peter & Sen, Maya, 2019. "Improving Supreme Court Forecasting Using Boosted Decision Trees," Political Analysis, Cambridge University Press, vol. 27(3), pages 381-387, July.

   Cited by:

   1. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.
   2. Reese, Benjamin F., 2024. "Estimating Unknown Cut-points in Regression Discontinuity and Kink Designs," SocArXiv 63tns, Center for Open Science.
   3. Netta Barak‐Corren & Yoav Kan‐Tor & Nelson Tebbe, 2022. "Examining the effects of antidiscrimination laws on children in the foster care and adoption systems," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 1003-1066, December.

5. Yuan Lo-Hua & Liu Anthony & Yeh Alec & Kaufman Aaron & Reece Andrew & Bull Peter & Franks Alex & Wang Sherrie & Illushin Dmitri & Bornn Luke, 2015. "A mixture-of-modelers approach to forecasting NCAA tournament outcomes," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(1), pages 13-27, March.

   Cited by:

   1. Alessandro Chessa & Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale & Alfonso Gebbia, 2023. "Complex networks for community detection of basketball players," Annals of Operations Research, Springer, vol. 325(1), pages 363-389, June.
   2. Ludden Ian G. & Khatibi Arash & King Douglas M. & Jacobson Sheldon H., 2020. "Models for generating NCAA men’s basketball tournament bracket pools," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 1-15, March.
   3. Paola Zuccolotto & Marco Sandri & Marica Manisera, 2023. "Spatial performance analysis in basketball with CART, random forest and extremely randomized trees," Annals of Operations Research, Springer, vol. 325(1), pages 495-519, June.
   4. Kovalchik, Stephanie & Reid, Machar, 2019. "A calibration method with dynamic updates for within-match forecasting of wins in tennis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 756-766.
   5. Jun Woo Kim & Mar Magnusen & Seunghoon Jeong, 2023. "March Madness prediction: Different machine learning approaches with non‐box score statistics," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 2223-2236, June.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-CMP: Computational Economics (1) 2022-09-26. Author is listed
2. NEP-POL: Positive Political Economics (1) 2022-09-26. Author is listed

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Aaron Russell Kaufman should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.