IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v7y2011i1n25.html
   My bibliography  Save this article

Clarifying the Role of Principal Stratification in the Paired Availability Design

Author

Listed:
  • Baker Stuart G

    (National Institutes of Health)

  • Lindeman Karen S

    (Johns Hopkins Medical Institutions)

  • Kramer Barnett S

    (National Institutes of Health)

Abstract

The paired availability design for historical controls postulated four classes corresponding to the treatment (old or new) a participant would receive if arrival occurred during either of two time periods associated with different availabilities of treatment. These classes were later extended to other settings and called principal strata. Judea Pearl asks if principal stratification is a goal or a tool and lists four interpretations of principal stratification. In the case of the paired availability design, principal stratification is a tool that falls squarely into Pearl’s interpretation of principal stratification as “an approximation to research questions concerning population averages.” We describe the paired availability design and the important role played by principal stratification in estimating the effect of receipt of treatment in a population using data on changes in availability of treatment. We discuss the assumptions and their plausibility. We also introduce the extrapolated estimate to make the generalizability assumption more plausible. By showing why the assumptions are plausible we show why the paired availability design, which includes principal stratification as a key component, is useful for estimating the effect of receipt of treatment in a population. Thus, for our application, we answer Pearl’s challenge to clearly demonstrate the value of principal stratification.

Suggested Citation

  • Baker Stuart G & Lindeman Karen S & Kramer Barnett S, 2011. "Clarifying the Role of Principal Stratification in the Paired Availability Design," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-11, May.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:25
    DOI: 10.2202/1557-4679.1338
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1338
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1338?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. Stuart G. Baker, 2011. "Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach," Biometrics, The International Biometric Society, vol. 67(1), pages 319-323, March.
    2. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.
    3. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    4. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, 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. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.

    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. Stuart G. Baker & Daniel J. Sargent & Marc Buyse & Tomasz Burzykowski, 2012. "Predicting Treatment Effect from Surrogate Endpoints and Historical Trials: An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time," Biometrics, The International Biometric Society, vol. 68(1), pages 248-257, March.
    2. Laura Forastiere & Fabrizia Mealli & Tyler J. VanderWeele, 2016. "Identification and Estimation of Causal Mechanisms in Clustered Encouragement Designs: Disentangling Bed Nets Using Bayesian Principal Stratification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 510-525, April.
    3. Andrea Mercatanti & Fan Li, 2017. "Do debit cards decrease cash demand?: causal inference and sensitivity analysis using principal stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 759-776, August.
    4. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    5. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, Institute of Labor Economics (IZA).
    6. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    7. James J. Heckman & Rodrigo Pinto, 2018. "Unordered Monotonicity," Econometrica, Econometric Society, vol. 86(1), pages 1-35, January.
    8. Weili Ding & Steven F. Lehrer, 2010. "Estimating Treatment Effects from Contaminated Multiperiod Education Experiments: The Dynamic Impacts of Class Size Reductions," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 31-42, February.
    9. Eva Deuchert & Martin Huber & Mark Schelker, 2019. "Direct and Indirect Effects Based on Difference-in-Differences With an Application to Political Preferences Following the Vietnam Draft Lottery," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(4), pages 710-720, October.
    10. Carlos A. Flores & Alfonso Flores-Lagunes, 2007. "Identification and Estimation of Casual Mechanisms and Net Effects of a Treatment," Working Papers 0706, University of Miami, Department of Economics.
    11. Deuchert, Eva & Eugster, Beatrix, 2019. "Income and substitution effects of a disability insurance reform," Journal of Public Economics, Elsevier, vol. 170(C), pages 1-14.
    12. Bia, Michela & Flores-Lagunes, Alfonso & Mercatanti, Andrea, 2018. "Evaluation of Language Training Programs in Luxembourg using Principal Stratification," GLO Discussion Paper Series 289, Global Labor Organization (GLO).
    13. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised Feb 2023.
    14. Shanshan Luo & Wei Li & Yangbo He, 2023. "Causal inference with outcomes truncated by death in multiarm studies," Biometrics, The International Biometric Society, vol. 79(1), pages 502-513, March.
    15. Kedagni, Desire, 2018. "Identifying Treatment Effects in the Presence of Confounded Types," ISU General Staff Papers 201809110700001056, Iowa State University, Department of Economics.
    16. Dawid Philip & Didelez Vanessa, 2012. ""Imagine a Can Opener"--The Magic of Principal Stratum Analysis," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-12, July.
    17. Masayuki Sawada, 2019. "Noncompliance in randomized control trials without exclusion restrictions," Papers 1910.03204, arXiv.org, revised Jun 2021.
    18. Huber, Martin & Steinmayr, Andreas, 2017. "A Framework for Separating Individual Treatment Effects From Spillover, Interaction, and General Equilibrium Effects," Rationality and Competition Discussion Paper Series 21, CRC TRR 190 Rationality and Competition.
    19. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    20. James J. Heckman & Rodrigo Pinto, 2023. "Econometric Causality: The Central Role of Thought Experiments," NBER Working Papers 31945, National Bureau of Economic Research, Inc.

    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:ijbist:v:7:y:2011:i:1:n:25. 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.