IDEAS home Printed from https://ideas.repec.org/a/bpj/statpp/v6y2015i1-2p39-75n1.html
   My bibliography  Save this article

Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments

Author

Listed:
  • Middleton Joel A.

    (Department of Political Science, University of California Berkeley, Berkeley, CA, USA)

  • Aronow Peter M.

    (Department of Political Science, Yale University, New Haven, CT, USA)

Abstract

Many estimators of the average treatment effect, including the difference-in-means, may be biased when clusters of units are allocated to treatment. This bias remains even when the number of units within each cluster grows asymptotically large. In this paper, we propose simple, unbiased, location-invariant, and covariate-adjusted estimators of the average treatment effect in experiments with random allocation of clusters, along with associated variance estimators. We then analyze a cluster-randomized field experiment on voter mobilization in the US, demonstrating that the proposed estimators have precision that is comparable, if not superior, to that of existing, biased estimators of the average treatment effect.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:statpp:v:6:y:2015:i:1-2:p:39-75:n:1
    DOI: 10.1515/spp-2013-0002
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/spp-2013-0002
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/spp-2013-0002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    3. Green, Donald P. & Vavreck, Lynn, 2008. "Analysis of Cluster-Randomized Experiments: A Comparison of Alternative Estimation Approaches," Political Analysis, Cambridge University Press, vol. 16(2), pages 138-152, April.
    4. Samii, Cyrus & Aronow, Peter M., 2012. "On equivalencies between design-based and regression-based variance estimators for randomized experiments," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 365-370.
    5. King, Gary & Roberts, Margaret E., 2015. "How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It," Political Analysis, Cambridge University Press, vol. 23(2), pages 159-179, April.
    6. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    7. Middleton, Joel A., 2008. "Bias of the regression estimator for experiments using clustered random assignment," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2654-2659, November.
    8. Luke W. Miratrix & Jasjeet S. Sekhon & Bin Yu, 2013. "Adjusting treatment effect estimates by post-stratification in randomized experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 369-396, March.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Federico Bugni & Ivan Canay & Azeem Shaikh & Max Tabord-Meehan, 2022. "Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes," Papers 2204.08356, arXiv.org, revised Apr 2024.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Peter Z. Schochet, "undated". "Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTs," Mathematica Policy Research Reports a0c005c003c242308a92c02dc, Mathematica Policy Research.
    3. 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.
    4. Haoge Chang & Joel Middleton & P. M. Aronow, 2021. "Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials," Papers 2110.08425, arXiv.org, revised Oct 2021.
    5. Hennessy Jonathan & Dasgupta Tirthankar & Miratrix Luke & Pattanayak Cassandra & Sarkar Pradipta, 2016. "A Conditional Randomization Test to Account for Covariate Imbalance in Randomized Experiments," Journal of Causal Inference, De Gruyter, vol. 4(1), pages 61-80, March.
    6. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    7. von Hinke, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2016. "Genetic markers as instrumental variables," Journal of Health Economics, Elsevier, vol. 45(C), pages 131-148.
    8. P. Dorian Owen, 2017. "Evaluating Ingenious Instruments for Fundamental Determinants of Long-Run Economic Growth and Development," Econometrics, MDPI, vol. 5(3), pages 1-33, September.
    9. Donald P. Green & Winston Lin & Claudia Gerber, 2018. "Optimal Allocation of Interviews to Baseline and Endline Surveys in Place-Based Randomized Trials and Quasi-Experiments," Evaluation Review, , vol. 42(4), pages 391-422, August.
    10. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    11. Ore Koren & Bumba Mukherjee, 2019. "Violent Repression as a Commitment Problem: Urbanization, Food Shortages, and Civilian Killings under Authoritarian Regimes," HiCN Working Papers 296, Households in Conflict Network.
    12. 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.
    13. Andersson, Josefine, 2018. "Financial incentives to work for disability insurance recipients - Sweden’s special rules for continuous deduction," Working Paper Series 2018:7, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    14. Taylor K. Odle, 2022. "Free to Spend? Institutional Autonomy and Expenditures on Executive Compensation, Faculty Salaries, and Research Activities," Research in Higher Education, Springer;Association for Institutional Research, vol. 63(1), pages 1-32, February.
    15. Joshua B. Gilbert & Zachary Himmelsbach & James Soland & Mridul Joshi & Benjamin W. Domingue, 2024. "Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory," Papers 2405.00161, arXiv.org, revised Aug 2024.
    16. Luis Costa & Vivek F. Farias & Patricio Foncea & Jingyuan (Donna) Gan & Ayush Garg & Ivo Rosa Montenegro & Kumarjit Pathak & Tianyi Peng & Dusan Popovic, 2023. "Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure," Interfaces, INFORMS, vol. 53(5), pages 336-349, September.
    17. Daniel Höhmann, 2017. "The effect of legislature size on public spending: evidence from a regression discontinuity design," Public Choice, Springer, vol. 173(3), pages 345-367, December.
    18. 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.
    19. Beatrice Magistro, 2020. "Financial literacy and support for free trade in the UK," The World Economy, Wiley Blackwell, vol. 43(8), pages 2050-2069, August.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:statpp:v:6:y:2015:i:1-2:p:39-75:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.