Preprints

  1. Auerbach, Jonathan, Martin Slawski, and Shixue Zhang. Tensor completion for causal inference with multivariate longitudinal data: A reevaluation of COVID-19 mandates.

  2. Auerbach, Jonathan. Estimating the number of street vendors in New York City.

  3. Auerbach, Jonathan, Claire McKay Bowen, Constance Citro, Steve Pierson, Nancy Potok, and Zachary Seeskin. The nation’s data at risk: Meeting America’s information needs for the 21st century.

  4. Biemer, Paul, Joseph Salvo, and Jonathan Auerbach. The quality of the 2020 Census: An independent assessment of Census Bureau activities critical to data quality.

  5. Auerbach, Jonathan and Christi Wilcox. Empowering students to assess the state of diversity, equity, and inclusion on campus.

  6. Auerbach, Jonathan, Yair Ghitza, and Andrew Gelman. A generational voting model for forecasting the 2020 American presidential election.

    7. Papers

  7. Elliott, Emerson, Jonathan Auerbach, Constance Citro, Daniel Elchert, Steve Pierson, Marilyn Seastrom, Thomas Snyder, Katherine Wallman, and James Woodworth. Bolstering education statistics to serve the nation. Statistics and Public Policy, forthcoming.

  8. Auerbach, Eric, Jonathan Auerbach, and Max Tabord-Meehan. Discussion of causal inference with misspecified exposure mappings: separating definitions and assumptions. Biometrika, vol. 111, no. 1. 2024.

  9. Hardesty, Madison and Jonathan Auerbach. Why do buildings skip the thirteenth floor? Significance, vol. 21, no. 2. 2024.

  10. Auerbach, Jonathan, David Kepplinger, and Nicholas Rios. What is data science? A closer look at science’s latest priority dispute. Real World Data Science. 2024. Reprinted. Significance, vol. 21, no. 2. 2024.

  11. Auerbach, Jonathan, May Aydin, Claire McKay Bowen, Constance Citro, Steve Pierson, Nancy Potok, and Zachary Seeskin. Discussion of independent review of the UK Statistics Authority. Journal of the Royal Statistical Society: Series A. 2024.

  12. Citro, Constance, Jonathan Auerbach, Katherine Smith Evans, Erica Groshen, J. Steven Landefeld, Jeri Mulrow, Thomas Petska, Steve Pierson, Nancy Potok, Charles Rothwell, John Thompson, James Woodworth, and Edward Wu. What protects the autonomy of the federal statistical agencies? An assessment of the procedures in place that protect the independence and objectivity of official statistics. Statistics and Public Policy, vol. 10, no. 1. 2023.

  13. Auerbach, Jonathan. Safeguarding facts in an era of disinformation: The case for independently monitoring the U.S. statistical system. Harvard Data Science Review, vol. 5, no. 3. 2023.

  14. Andrade, Chris, Jonathan Auerbach, Icaro Bacelar, Hane Lee, Angela Tan, Mariana Vazquez, and Owen Ward. Does it pay to park in front of a fire hydrant? Significance, vol. 20, no. 1. 2023.

  15. Ghitza, Yair, Andrew Gelman, and Jonathan Auerbach. The Great Society, Reagan’s Revolution, and generations of presidential voting. American Journal of Political Science, vol. 67, no. 3. 2023.

  16. Auerbach, Jonathan and Catherine Elizabeth DeLazzero. Linked data detail a gender gap in STEM that persists across time and place. Harvard Data Science Review, vol. 4, no. 2. 2022.

  17. Wolkovich, E., J. Auerbach, C. Chamberlain, D. Buonaiuto, A. Ettinger, I. Morales-Castilla, and A. Gelman. A simple explanation for declining temperature sensitivity with warming. Global Change Biology, vol. 27, no. 20. 2021.

  18. Auerbach, Jonathan and Steve Pierson. Does voting by mail increase fraud? Estimating the change in reported voter fraud when states switch to elections by mail. Statistics and Public Policy, vol. 8, no. 1. 2021.

  19. Auerbach, Jonathan, Christopher Eshleman, and Rob Trangucci. A hierarchical Bayes approach to adjust for selection bias in before-after analyses of Vision Zero policies. Computational Statistics, vol. 36, no. 3. 2021.

  20. Auerbach, Jonathan and Steve Pierson. What would happen if the deadline for the 2020 Census data collection operation changed? American Statistical Association Technical Report. 2020.

  21. Auerbach, Jonathan and Phyllis Wan. Forecasting the urban skyline with extreme value theory. International Journal of Forecasting, vol. 36, no. 3. 2020.

  22. Auerbach, Jonathan, Quentin Brummet, John Czajka, George C. Hough Jr, Eddie Hunsinger, and Joseph Salvo. Will administrative data save government surveys? Significance, vol. 16, no. 5. 2019.

  23. Auerbach, Jonathan, Richard Howey, Lai Jiang, Anne Justice, Liming Li, Karim Oualkacha, Sergi Sayols-Baixeras, and Stella Aslibekyan. Causal modeling in a multi-omics setting: Insights from Genetic Analysis Workshop 20. BMC Genetics, vol. 19, no. 1. 2018.

  24. Hsu, Yayun, Jonathan Auerbach, Tian Zheng, and Shaw-hwa Lo. Coping with family structure in genome-wide association studies: A comparative evaluation. BMC Proceedings, vol. 12, no. 9. 2018.

  25. Patton, Desmond, Owen Rambow, Jonathan Auerbach, Kevin Li, and William Frey. Expressions of loss predict aggressive comments on Twitter among gang involved youth in Chicago. Nature Partner Journal: Digital Medicine, vol. 1, no. 1. 2018.

  26. Auerbach, Jonathan. Are New York City drivers more likely to get a ticket at the end of the month? Significance, vol. 14, no. 4. 2017.

  27. Gelman, Andrew and Jonathan Auerbach. Age-aggregation bias in mortality trends. Proceedings of the National Academy of Sciences, vol. 113, no. 7. 2016.

  28. Auerbach, Jonathan, Michael Agne, Rachel Fan, Adeline Lo, Shaw-Hwa Lo, Tian Zheng, and Pei Wang. Identifying regions of disease-related variants in admixed populations with the summation partition approach. BMC Proceedings, vol. 10, no. 7. 2016.

  29. Lo, Adeline, Michael Agne, Jonathan Auerbach, Rachel Fan, Shaw-Hwa Lo, Pei Wang, and Tian Zheng. Network-guided interaction mining for the blood pressure phenotype of unrelated individuals in genetic analysis workshop 19. BMC Proceedings, vol. 10, no. 7. 2016.

  30. König, Inke R., Jonathan Auerbach, Damian Gola, Elizabeth Held, Emily R. Holzinger, Marc-André Legault, Rui Sun, Nathan Tintle, and Hsin-Chou Yang. Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19. BMC Genetics, vol. 17, no. 2. 2016.

  31. Auerbach, Jonathan. Does New York City really have as many rats as people? Significance, vol. 11, no. 4. 2014.

    32. Conferences

  32. Auerbach, Jonathan and Breck Baldwin. Getting the lead out: Does New York City's childhood lead testing make statistical sense? StanCon. 2019.

  33. Auerbach, Jonathan. Does the New York City Police Department rely on quotas? StanCon. 2018.

  34. Auerbach, Jonathan, Timothy Jones, and Robin Winstanley. Predicting New York City school enrollment. StanCon. 2018.

  35. Auerbach, Jonathan and Rob Trangucci. Twelve cities: Does lowering speed limits save pedestrian lives? StanCon. 2017.

    36. Opinions

  36. Auerbach, Jonathan. Why top-down testing every kid for lead poisoning can backfire. New York Daily News. 2019.

  37. Auerbach, Jonathan. No urban legend: Stats show NYPD traffic tickets more likely at month's end. New York Daily News. 2017.

  38. Auerbach, Jonathan. De Blasio wants to dramatically reduce NYC’s rat population. Slate Magazine. 2017.

  39. Auerbach, Jonathan and Andrew Gelman. Stop saying White mortality is rising. Slate Magazine. 2017.

  40. Auerbach, Jonathan. Open data: More questions than answers. Amstat News. 2014.

    41. Tutorials

  41. Auerbach, Jonathan. A demonstration of the law of the flowering plants. Real World Data Science. 2023.

  42. Auerbach, Jonathan, Christopher Eshleman, and Jeff Chen. Diving into New York City's service requests, part 1. Commerce Data Service, U.S. Department of Commerce. 2016.

  43. Auerbach, Jonathan, Christopher Eshleman, and Jeff Chen. Diving into New York City's service requests, part 2. Commerce Data Service, U.S. Department of Commerce. 2016.