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Causal Effects of Perceived Immutable Characteristics

Author

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  • D. James Greiner

    (Harvard Law School)

  • Donald B. Rubin

    (Harvard University)

Abstract

Despite their ubiquity, observational studies to infer the causal effect of a so-called immutable characteristic, such as race or sex, have struggled for coherence, given the unavailability of a manipulation analogous to a “treatment” in a randomized experiment and the danger of posttreatment bias. We demonstrate that a shift in focus from actual traits to perceptions of them can address both of these issues while facilitating articulation of other critical concepts, particularly the timing of treatment assignment. We illustrate concepts by discussing the designs of various studies of the role of race in trial court death penalty decisions. © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • D. James Greiner & Donald B. Rubin, 2011. "Causal Effects of Perceived Immutable Characteristics," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 775-785, August.
    • Handle: RePEc:tpr:restat:v:93:y:2011:i:3:p:775-785
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    Cited by:

    1. Chowdhury, Shyamal & Ooi, Evarn & Slonim, Robert, 2017. "Racial discrimination and white first name adoption: a field experiment in the Australian labour market," Working Papers 2017-15, University of Sydney, School of Economics.
    2. Tymon Sloczynski, 2018. "Average Gaps and Oaxaca–Blinder Decompositions: A Cautionary Tale about Regression Estimates of Racial Differences in Labor Market Outcomes," Working Papers 127, Brandeis University, Department of Economics and International Business School.
    3. Borra, Cristina & Iacovou, Maria & Sevilla, Almudena, 2023. "Adolescent development and the math gender gap," European Economic Review, Elsevier, vol. 158(C).
    4. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.
    5. Steven F. Lehrer & Weili Ding, 2017. "Are genetic markers of interest for economic research?," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-23, December.
    6. Olsson, Ola & Siba, Eyerusalem, 2013. "Ethnic cleansing or resource struggle in Darfur? An empirical analysis," Journal of Development Economics, Elsevier, vol. 103(C), pages 299-312.
    7. Tymon Słoczyński, 2020. "Average Gaps and Oaxaca–Blinder Decompositions: A Cautionary Tale about Regression Estimates of Racial Differences in Labor Market Outcomes," ILR Review, Cornell University, ILR School, vol. 73(3), pages 705-729, May.
    8. Pierre Bentata & Romain Espinosa & Yolande Hiriart, 2019. "Correction Activities by France’s Supreme Courts and Control over their Dockets," Revue d'économie politique, Dalloz, vol. 129(2), pages 169-204.
    9. Sloczynski, Tymon, 2015. "Average Wage Gaps and Oaxaca–Blinder Decompositions," IZA Discussion Papers 9036, Institute of Labor Economics (IZA).
    10. Martin Huber, 2015. "Causal Pitfalls in the Decomposition of Wage Gaps," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 179-191, April.
    11. Harris, J. Andrew & van der Windt, Peter, 2023. "Empowering women or increasing response bias? Experimental evidence from Congo," Journal of Development Economics, Elsevier, vol. 164(C).
    12. Pierre Bentata & Yolande Hiriart, 2015. "Biased Judges: Evidence from French Environmental Cases," Working Papers hal-01377922, HAL.
    13. Joshua Grossman & Julian Nyarko & Sharad Goel, 2023. "Racial bias as a multi‐stage, multi‐actor problem: An analysis of pretrial detention," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 20(1), pages 86-133, March.
    14. Myoung-jae Lee, 2017. "Extensive and intensive margin effects in sample selection models: racial effects on wages," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 817-839, June.
    15. Kim, Jae Yeon, 2021. "Power, Hate Speech, Machine Learning, and Intersectional Approach," SocArXiv chvgp, Center for Open Science.
    16. Martin Huber & Anna Solovyeva, 2020. "On the Sensitivity of Wage Gap Decompositions," Journal of Labor Research, Springer, vol. 41(1), pages 1-33, June.
    17. Arpino, Bruno & Mattei, Alessandra, 2013. "Assessing the Impact of Financial Aids to Firms: Causal Inference in the presence of Interference," MPRA Paper 51795, University Library of Munich, Germany.
    18. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.

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