IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v74y2018i2p403-406.html
   My bibliography  Save this article

Discussion of “Data†driven confounder selection via Markov and Bayesian networks†by Häggström

Author

Listed:
  • Thomas S. Richardson
  • James M. Robins
  • Linbo Wang

Abstract

No abstract is available for this item.

Suggested Citation

  • Thomas S. Richardson & James M. Robins & Linbo Wang, 2018. "Discussion of “Data†driven confounder selection via Markov and Bayesian networks†by Häggström," Biometrics, The International Biometric Society, vol. 74(2), pages 403-406, June.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:403-406
    DOI: 10.1111/biom.12784
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.12784
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.12784?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. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    2. van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
    3. Tyler J. VanderWeele & Ilya Shpitser, 2011. "A New Criterion for Confounder Selection," Biometrics, The International Biometric Society, vol. 67(4), pages 1406-1413, December.
    4. James M. Robins, 2003. "Uniform consistency in causal inference," Biometrika, Biometrika Trust, vol. 90(3), pages 491-515, September.
    5. Xavier De Luna & Ingeborg Waernbaum & Thomas S. Richardson, 2011. "Covariate selection for the nonparametric estimation of an average treatment effect," Biometrika, Biometrika Trust, vol. 98(4), pages 861-875.
    Full references (including those not matched with items on IDEAS)

    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. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
    2. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2021. "Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison," Stats, MDPI, vol. 4(2), pages 1-21, June.
    3. David Cheng & Abhishek Chakrabortty & Ashwin N. Ananthakrishnan & Tianxi Cai, 2020. "Estimating average treatment effects with a double‐index propensity score," Biometrics, The International Biometric Society, vol. 76(3), pages 767-777, September.
    4. Yongnam Kim, 2019. "The Causal Structure of Suppressor Variables," Journal of Educational and Behavioral Statistics, , vol. 44(4), pages 367-389, August.
    5. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
    6. Brandon Koch & David M. Vock & Julian Wolfson, 2018. "Covariate selection with group lasso and doubly robust estimation of causal effects," Biometrics, The International Biometric Society, vol. 74(1), pages 8-17, March.
    7. Matthew Cefalu & Francesca Dominici & Nils Arvold & Giovanni Parmigiani, 2017. "Model averaged double robust estimation," Biometrics, The International Biometric Society, vol. 73(2), pages 410-421, June.
    8. Persson, Emma & Häggström, Jenny & Waernbaum, Ingeborg & de Luna, Xavier, 2017. "Data-driven algorithms for dimension reduction in causal inference," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 280-292.
    9. Xun Lu, 2015. "A Covariate Selection Criterion for Estimation of Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 506-522, October.
    10. Xu Qin & Jonah Deutsch & Guanglei Hong, 2021. "Unpacking Complex Mediation Mechanisms And Their Heterogeneity Between Sites In A Job Corps Evaluation," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(1), pages 158-190, January.
    11. Leonard Henckel & Emilija Perković & Marloes H. Maathuis, 2022. "Graphical criteria for efficient total effect estimation via adjustment in causal linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 579-599, April.
    12. Edward H. Kennedy & Sivaraman Balakrishnan, 2018. "Discussion of “Data†driven confounder selection via Markov and Bayesian networks†by Jenny Häggström," Biometrics, The International Biometric Society, vol. 74(2), pages 399-402, June.
    13. Häggström, Jenny & Persson, Emma & Waernbaum, Ingeborg & de Luna, Xavier, 2015. "CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i01).
    14. Bryan Keller, 2020. "Variable Selection for Causal Effect Estimation: Nonparametric Conditional Independence Testing With Random Forests," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 119-142, April.
    15. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    16. Jenny Häggström, 2018. "Data†driven confounder selection via Markov and Bayesian networks," Biometrics, The International Biometric Society, vol. 74(2), pages 389-398, June.
    17. Sean Yiu & Li Su, 2018. "Covariate association eliminating weights: a unified weighting framework for causal effect estimation," Biometrika, Biometrika Trust, vol. 105(3), pages 709-722.
    18. Uehleke, Reinhard & Petrick, Martin & Hüttel, Silke, 2022. "Evaluations of agri-environmental schemes based on observational farm data: The importance of covariate selection," Land Use Policy, Elsevier, vol. 114(C).
    19. Chen, Pu & Hsiao, Chih-Ying, 2010. "Looking behind Granger causality," MPRA Paper 24859, University Library of Munich, Germany.
    20. Stenberg, Anders & Westerlund, Olle, 2016. "Flexibility at a cost – Should governments stimulate tertiary education for adults?," The Journal of the Economics of Ageing, Elsevier, vol. 7(C), pages 69-86.

    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:biomet:v:74:y:2018:i:2:p:403-406. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.