IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v178y2015i3p757-778.html
   My bibliography  Save this article

From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects

Author

Listed:
  • Erin Hartman
  • Richard Grieve
  • Roland Ramsahai
  • Jasjeet S. Sekhon

Abstract

type="main" xml:id="rssa12094-abs-0001"> Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive the assumptions that are required to identify population average treatment effects from RCTs. We provide placebo tests, which formally follow from the identifying assumptions and can assess whether they hold. We offer new research designs for estimating population effects that use non-randomized studies to adjust the RCT data. This approach is considered in a cost-effectiveness analysis of a clinical intervention: pulmonary artery catheterization.

Suggested Citation

  • Erin Hartman & Richard Grieve & Roland Ramsahai & Jasjeet S. Sekhon, 2015. "From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 757-778, June.
    • Handle: RePEc:bla:jorssa:v:178:y:2015:i:3:p:757-778
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssa.2015.178.issue-3
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    Citations

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


    Cited by:

    1. Tarek Azzam & Michael Bates & David Fairris, 2019. "Do Learning Communities Increase First Year College Retention? Testing Sample Selection and External Validity of Randomized Control Trials," Working Papers 202002, University of California at Riverside, Department of Economics.
    2. Rui Chen & Guanhua Chen & Menggang Yu, 2023. "Entropy balancing for causal generalization with target sample summary information," Biometrics, The International Biometric Society, vol. 79(4), pages 3179-3190, December.
    3. Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2021. "From Local to Global: External Validity in a Fertility Natural Experiment," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 217-243, January.
    4. Fan Li & Ashley L. Buchanan & Stephen R. Cole, 2022. "Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 669-697, June.
    5. James Bisbee & Rajeev Dehejia & Cristian Pop-Eleches & Cyrus Samii, 2017. "Local Instruments, Global Extrapolation: External Validity of the Labor Supply-Fertility Local Average Treatment Effect," Journal of Labor Economics, University of Chicago Press, vol. 35(S1), pages 99-147.
    6. Ottoboni Kellie N. & Poulos Jason V., 2020. "Estimating population average treatment effects from experiments with noncompliance," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 108-130, January.
    7. Elizabeth Tipton, 2021. "Beyond generalization of the ATE: Designing randomized trials to understand treatment effect heterogeneity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 504-521, April.
    8. Vishal Gupta & Brian Rongqing Han & Song-Hee Kim & Hyung Paek, 2020. "Maximizing Intervention Effectiveness," Management Science, INFORMS, vol. 66(12), pages 5576-5598, December.
    9. Marco Morucci & Vittorio Orlandi & Harsh Parikh & Sudeepa Roy & Cynthia Rudin & Alexander Volfovsky, 2023. "A Double Machine Learning Approach to Combining Experimental and Observational Data," Papers 2307.01449, arXiv.org.
    10. Ashley L. Buchanan & Michael G. Hudgens & Stephen R. Cole & Katie R. Mollan & Paul E. Sax & Eric S. Daar & Adaora A. Adimora & Joseph J. Eron & Michael J. Mugavero, 2018. "Generalizing evidence from randomized trials using inverse probability of sampling weights," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1193-1209, October.
    11. Devin Caughey & Sara Chatfield, 2020. "Causal inference and American political development: contrasts and complementarities," Public Choice, Springer, vol. 185(3), pages 359-376, December.
    12. Xinyu Li & Wang Miao & Fang Lu & Xiao‐Hua Zhou, 2023. "Improving efficiency of inference in clinical trials with external control data," Biometrics, The International Biometric Society, vol. 79(1), pages 394-403, March.
    13. Alejandro Noriega & Alex Pentland, 2020. "Representativity and Networked Interference in Data-Rich Field Experiments: A Large-Scale RCT in Rural Mexico," The World Bank Economic Review, World Bank, vol. 34(Supplemen), pages 35-39.
    14. Daido Kido, 2022. "Distributionally Robust Policy Learning with Wasserstein Distance," Papers 2205.04637, arXiv.org, revised Aug 2022.
    15. Azzam, Tarek & Bates, Michael D. & Fairris, David, 2022. "Do learning communities increase first year college retention? Evidence from a randomized control trial," Economics of Education Review, Elsevier, vol. 89(C).
    16. Dasom Lee & Shu Yang & Lin Dong & Xiaofei Wang & Donglin Zeng & Jianwen Cai, 2023. "Improving trial generalizability using observational studies," Biometrics, The International Biometric Society, vol. 79(2), pages 1213-1225, June.
    17. Takahiro Hoshino & Ryosuke Igari, 2017. "Quasi-Bayesian Inference for Latent Variable Models with External Information: Application to generalized linear mixed models for biased data," Keio-IES Discussion Paper Series 2017-014, Institute for Economics Studies, Keio University.
    18. Adam Steventon & Richard Grieve & Martin Bardsley, 2015. "An Approach to Assess Generalizability in Comparative Effectiveness Research," Medical Decision Making, , vol. 35(8), pages 1023-1036, November.
    19. David M. Phillippo & Anthony E. Ades & Sofia Dias & Stephen Palmer & Keith R. Abrams & Nicky J. Welton, 2018. "Methods for Population-Adjusted Indirect Comparisons in Health Technology Appraisal," Medical Decision Making, , vol. 38(2), pages 200-211, February.
    20. Kellie Ottoboni & Jason Poulos, 2019. "Estimating population average treatment effects from experiments with noncompliance," Papers 1901.02991, arXiv.org, revised Aug 2020.
    21. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    22. Isaiah Andrews & Emily Oster, 2017. "A Simple Approximation for Evaluating External Validity Bias," NBER Working Papers 23826, National Bureau of Economic Research, Inc.
    23. Bin Tang & Te-Tien Ting & Chyi-In Wu & Yue Ma & Di Mo & Wei-Ting Hung & Scott Rozelle, 2020. "The Impact of Online Computer Assisted Learning at Home for Disadvantaged Children in Taiwan: Evidence from a Randomized Experiment," Sustainability, MDPI, vol. 12(23), pages 1-16, December.
    24. Julien Forder & Florin Vadean & Stacey Rand & Juliette Malley, 2018. "The impact of long‐term care on quality of life," Health Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 43-58, March.
    25. Naoki Egami & Erin Hartman, 2021. "Covariate selection for generalizing experimental results: Application to a large‐scale development program in Uganda," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1524-1548, October.

    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:bla:jorssa:v:178:y:2015:i:3:p:757-778. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.