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Jake Bowers

Personal Details

First Name:Jake
Middle Name:
Last Name:Bowers
Suffix:
RePEc Short-ID:pbo364
http://www.umich.edu/~jwbowers

Affiliation

(99%) University of Illinois at Urbana-Champaign, Dept of Political Science

http://www.pol.uiuc.edu/
USA, Urbana

(1%) National Center for Supercomputing Applications

http://www.ncsa.uiuc.edu
USA, Urbana

Research output

as
Jump to: Articles Software

Articles

  1. Hansen, Ben B. & Bowers, Jake, 2009. "Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 873-885.

Software components

  1. Jake Bowers & Mark Fredrickson & Ben Hansen, 2008. "XBALANCE: Stata module to compute standardized differences for stratified comparisons via R," Statistical Software Components S456989, Boston College Department of Economics, revised 23 Jan 2009.

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.

Articles

  1. Hansen, Ben B. & Bowers, Jake, 2009. "Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 873-885.

    Cited by:

    1. Luke Keele & Dylan Small & Richard Grieve, 2017. "Randomization-based instrumental variables methods for binary outcomes with an application to the ‘IMPROVE’ trial," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 569-586, February.
    2. Joel A. Middleton, 2021. "Unifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design," Papers 2109.09220, arXiv.org.
    3. Middleton Joel A. & Aronow Peter M., 2015. "Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments," Statistics, Politics and Policy, De Gruyter, vol. 6(1-2), pages 39-75, December.
    4. Arzi Adbi & Chirantan Chatterjee & Matej Drev & Anant Mishra, 2019. "When the Big One Came: A Natural Experiment on Demand Shock and Market Structure in India's Influenza Vaccine Markets," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 810-832, April.
    5. Aronow Peter M. & Middleton Joel A., 2013. "A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 135-154, June.
    6. Adam C. Sales & Ben B. Hansen, 2020. "Limitless Regression Discontinuity," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 143-174, April.
    7. Ben Yishay, Ariel & Fraker, Andrew & Guiteras, Raymond & Palloni, Giordano & Shah, Neil Buddy & Shirrell, Stuart & Wang, Paul, 2017. "Microcredit and willingness to pay for environmental quality: Evidence from a randomized-controlled trial of finance for sanitation in rural Cambodia," Journal of Environmental Economics and Management, Elsevier, vol. 86(C), pages 121-140.
    8. Peter John Loewen & Daniel Rubenson & Leonard Wantchekon, 2010. "Help Me Help You: Conducting Field Experiments with Political Elites," The ANNALS of the American Academy of Political and Social Science, , vol. 628(1), pages 165-175, March.
    9. Hyunseung Kang & Laura Peck & Luke Keele, 2018. "Inference for instrumental variables: a randomization inference approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1231-1254, October.
    10. Ben B. Hansen & Paul R. Rosenbaum & Dylan S. Small, 2014. "Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 133-144, March.
    11. van der Laan Mark J. & Petersen Maya & Zheng Wenjing, 2013. "Estimating the Effect of a Community-Based Intervention with Two Communities," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 83-106, June.

Software components

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More information

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Statistics

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Corrections

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