IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v41y2016i5p463-480.html
   My bibliography  Save this article

Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs

Author

Listed:
  • Fabrizia Mealli

    (University of Florence)

  • Barbara Pacini

    (University of Pisa)

  • Elena Stanghellini

    (University of Perugia)

Abstract

Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial and bounds (or sets) for the causal effects are established. In the presence of a secondary outcome, recent results exist to sharpen the bounds that exploit conditional independence assumptions. More general results, though not embedded in a causal framework, can be found in concentration graphical models with a latent variable. The aim of this article is to establish a link between the two settings and to show that adapting and extending results pertaining to concentration graphical models can help achieving identification of principal casual effects in studies when more than one additional outcome is available. Model selection criteria are also suggested. An empirical illustrative example is provided, using data from a real social experiment.

Suggested Citation

  • Fabrizia Mealli & Barbara Pacini & Elena Stanghellini, 2016. "Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs," Journal of Educational and Behavioral Statistics, , vol. 41(5), pages 463-480, October.
    • Handle: RePEc:sae:jedbes:v:41:y:2016:i:5:p:463-480
    DOI: 10.3102/1076998616646199
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998616646199
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998616646199?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
    ---><---

    References listed on IDEAS

    as
    1. Paolo Frumento & Fabrizia Mealli & Barbara Pacini & Donald B. Rubin, 2012. "Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment, and Missing Outcome Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 450-466, June.
    2. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    3. repec:mpr:mprres:7071 is not listed on IDEAS
    4. Fabrizia Mealli & Barbara Pacini, 2013. "Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1120-1131, September.
    5. Leonardo Grilli & Fabrizia Mealli, 2008. "Nonparametric Bounds on the Causal Effect of University Studies on Job Opportunities Using Principal Stratification," Journal of Educational and Behavioral Statistics, , vol. 33(1), pages 111-130, March.
    6. Martin Huber, 2012. "Identification of Average Treatment Effects in Social Experiments Under Alternative Forms of Attrition," Journal of Educational and Behavioral Statistics, , vol. 37(3), pages 443-474, June.
    7. Alessandra Mattei & Fabrizia Mealli & Barbara Pacini, 2014. "Identification of causal effects in the presence of nonignorable missing outcome values," Biometrics, The International Biometric Society, vol. 70(2), pages 278-288, June.
    8. Zhang, Junni L. & Rubin, Donald B. & Mealli, Fabrizia, 2009. "Likelihood-Based Analysis of Causal Effects of Job-Training Programs Using Principal Stratification," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 166-176.
    9. 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.
    10. repec:mpr:mprres:2952 is not listed on IDEAS
    11. Guido W. Imbens & Donald B. Rubin, 1997. "Estimating Outcome Distributions for Compliers in Instrumental Variables Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 555-574.
    12. Richard McHugh, 1956. "Efficient estimation and local identification in latent class analysis," Psychometrika, Springer;The Psychometric Society, vol. 21(4), pages 331-347, December.
    13. Allman Elizabeth S. & Rhodes John A. & Stanghellini Elena & Valtorta Marco, 2015. "Parameter Identifiability of Discrete Bayesian Networks with Hidden Variables," Journal of Causal Inference, De Gruyter, vol. 3(2), pages 189-205, September.
    14. Dean Follmann, 2006. "Augmented Designs to Assess Immune Response in Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(4), pages 1161-1169, December.
    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. Silvia Noirjean & Mario Biggeri & Laura Forastiere & Fabrizia Mealli & Maria Nannini, 2023. "Estimating causal effects of community health financing via principal stratification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1317-1350, October.
    2. Lupparelli, Monia & Mattei, Alessandra, 2020. "Joint and marginal causal effects for binary non-independent outcomes," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    3. Avi Feller & Fabrizia Mealli & Luke Miratrix, 2017. "Principal Score Methods: Assumptions, Extensions, and Practical Considerations," Journal of Educational and Behavioral Statistics, , vol. 42(6), pages 726-758, December.

    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. Zhichao Jiang & Peng Ding & Zhi Geng, 2016. "Principal causal effect identification and surrogate end point evaluation by multiple trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 829-848, September.
    2. 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.
    3. Zhichao Jiang & Shu Yang & Peng Ding, 2022. "Multiply robust estimation of causal effects under principal ignorability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1423-1445, September.
    4. Jiannan Lu & Peng Ding & Tirthankar Dasgupta, 2018. "Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 540-567, October.
    5. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    6. 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).
    7. Fan Yang & Dylan S. Small, 2016. "Using post-outcome measurement information in censoring-by-death problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 299-318, January.
    8. Fan Yang & Peng Ding, 2018. "Using survival information in truncation by death problems without the monotonicity assumption," Biometrics, The International Biometric Society, vol. 74(4), pages 1232-1239, December.
    9. Avi Feller & Fabrizia Mealli & Luke Miratrix, 2017. "Principal Score Methods: Assumptions, Extensions, and Practical Considerations," Journal of Educational and Behavioral Statistics, , vol. 42(6), pages 726-758, December.
    10. Stephens Alisa & Keele Luke & Joffe Marshall, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1-17, September.
    11. Stephens Alisa & Joffe Marshall & Keele Luke, 2016. "Generalized Structural Mean Models for Evaluating Depression as a Post-treatment Effect Modifier of a Jobs Training Intervention," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1, September.
    12. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    13. Hans Fricke & Markus Frölich & Martin Huber & Michael Lechner, 2020. "Endogeneity and non‐response bias in treatment evaluation – nonparametric identification of causal effects by instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 481-504, August.
    14. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.
    15. Alessandra Mattei & Fabrizia Mealli & Barbara Pacini, 2014. "Identification of causal effects in the presence of nonignorable missing outcome values," Biometrics, The International Biometric Society, vol. 70(2), pages 278-288, June.
    16. Chanmin Kim & Lucas R. F. Henneman & Christine Choirat & Corwin M. Zigler, 2020. "Health effects of power plant emissions through ambient air quality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1677-1703, October.
    17. Eduardo Fé, 2021. "Pension eligibility rules and the local causal effect of retirement on cognitive functioning," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 812-841, July.
    18. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    19. Peng Ding & Jiannan Lu, 2017. "Principal stratification analysis using principal scores," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 757-777, June.
    20. German Blanco & Carlos A. Flores & Alfonso Flores-Lagunes, 2013. "Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 48(3), pages 659-701.

    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:sae:jedbes:v:41:y:2016:i:5:p:463-480. 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: SAGE Publications (email available below). General contact details of provider: .

    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.