IDEAS home Printed from https://ideas.repec.org/r/cup/polals/v24y2016i03p324-338_01.html

Why Experimenters Might Not Always Want to Randomize, and What They Could Do Instead

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Abhijit Banerjee & Sylvain Chassang & Sergio Montero & Erik Snowberg, 2017. "A Theory of Experimenters," NBER Working Papers 23867, National Bureau of Economic Research, Inc.
  2. Moshe Justman, 2016. "Economic Research and Education Policy: Project STAR and Class Size Reduction," Melbourne Institute Working Paper Series wp2016n37, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  3. Timothy B. Armstrong & Shu Shen, 2013. "Inference on Optimal Treatment Assignments," Cowles Foundation Discussion Papers 1927RR, Cowles Foundation for Research in Economics, Yale University, revised Apr 2015.
  4. Davide Viviano, 2020. "Experimental Design under Network Interference," Papers 2003.08421, arXiv.org, revised Nov 2025.
  5. Sven Resnjanskij & Jens Ruhose & Simon Wiederhold & Ludger Woessmann & Katharina Wedel, 2024. "Can Mentoring Alleviate Family Disadvantage in Adolescence? A Field Experiment to Improve Labor Market Prospects," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 1013-1062.
  6. Justman, Moshe, 2018. "Randomized controlled trials informing public policy: Lessons from project STAR and class size reduction," European Journal of Political Economy, Elsevier, vol. 54(C), pages 167-174.
  7. Kaushik Basu & David Rosenblatt & Claudia Sepulveda, 2019. "The State of Economics, the State of the World," World Bank Publications - Books, The World Bank Group, number 36844, April.
  8. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020. "Optimal data collection for randomized control trials," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
  9. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
  10. Aristotelis Epanomeritakis & Davide Viviano, 2025. "Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence," Papers 2510.23434, arXiv.org, revised Dec 2025.
  11. Esposito Acosta,Bruno Nicola & Sautmann,Anja, 2022. "Adaptive Experiments for Policy Choice : Phone Calls for Home Reading in Kenya," Policy Research Working Paper Series 10098, The World Bank.
  12. Riener, Gerhard & Schneider, Sebastian & Wagner, Valentin, 2020. "Addressing validity and generalizability concerns in field experiments," DICE Discussion Papers 345, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
  13. Max Cytrynbaum & Fredrik Savje, 2026. "Coupling Designs for Randomized Experiments with Complex Treatments," Papers 2604.09858, arXiv.org.
  14. Yusuke Narita, 2018. "Experiment-as-Market: Incorporating Welfare into Randomized Controlled Trials," Cowles Foundation Discussion Papers 2127r, Cowles Foundation for Research in Economics, Yale University, revised May 2019.
  15. Wang, Yuhao & Li, Xinran, 2025. "Asymptotic theory of the best-choice rerandomization using the Mahalanobis distance," Journal of Econometrics, Elsevier, vol. 251(C).
  16. Fryer, Roland G., 2016. "Information, non-financial incentives, and student achievement: Evidence from a text messaging experiment," Journal of Public Economics, Elsevier, vol. 144(C), pages 109-121.
  17. Bai, Yuehao, 2023. "Why randomize? Minimax optimality under permutation invariance," Journal of Econometrics, Elsevier, vol. 232(2), pages 565-575.
  18. Aufenanger, Tobias, 2018. "Treatment allocation for linear models," FAU Discussion Papers in Economics 14/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2018.
  19. Roland G. Fryer, Jr, 2013. "Information and Student Achievement: Evidence from a Cellular Phone Experiment," NBER Working Papers 19113, National Bureau of Economic Research, Inc.
  20. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.
  21. Eszter Czibor & David Jimenez‐Gomez & John A. List, 2019. "The Dozen Things Experimental Economists Should Do (More of)," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 371-432, October.
  22. Brendan Kline & Matthew A. Masten, 2025. "Finite Population Identification and Design-Based Sensitivity Analysis," Papers 2504.14127, arXiv.org, revised Mar 2026.
  23. Mogues, Tewodaj & Van Campenhout, Bjorn & Miehe, Caroline & Kabunga, Nassul, 2023. "The impact of community-based monitoring on public service delivery: A randomized control trial in Uganda," World Development, Elsevier, vol. 172(C).
  24. Aufenanger, Tobias, 2017. "Machine learning to improve experimental design," FAU Discussion Papers in Economics 16/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2017.
  25. Max Cytrynbaum, 2021. "Optimal Stratification of Survey Experiments," Papers 2111.08157, arXiv.org, revised Aug 2023.
  26. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
  27. Alfredo Di Tillio & Marco Ottaviani & Peter Norman Sørensen, 2021. "Strategic Sample Selection," Econometrica, Econometric Society, vol. 89(2), pages 911-953, March.
  28. Yiping Lu & Jiajin Li & Lexing Ying & Jose Blanchet, 2022. "Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls," Papers 2211.15241, arXiv.org.
  29. Abhijit Banerjee & Sylvain Chassang & Erik Snowberg, 2016. "Decision Theoretic Approaches to Experiment Design and External Validity," NBER Working Papers 22167, National Bureau of Economic Research, Inc.
  30. Drazen, Allan & Dreber, Anna & Ozbay, Erkut Y. & Snowberg, Erik, 2021. "Journal-based replication of experiments: An application to “Being Chosen to Lead”," Journal of Public Economics, Elsevier, vol. 202(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.