IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v3y2015i2p139-155n1.html
   My bibliography  Save this article

Balancing Score Adjusted Targeted Minimum Loss-based Estimation

Author

Listed:
  • Lendle Samuel David

    (Group in Biostatistics, University of California, Berkeley, Berkeley, CA, USA)

  • Fireman Bruce

    (Division of Research, Kaiser Permanente, Oakland, CA, USA)

  • van der Laan Mark J.

    (Group in Biostatistics, University of California, Berkeley, Berkeley, CA, USA)

Abstract

Adjusting for a balancing score is sufficient for bias reduction when estimating causal effects including the average treatment effect and effect among the treated. Estimators that adjust for the propensity score in a nonparametric way, such as matching on an estimate of the propensity score, can be consistent when the estimated propensity score is not consistent for the true propensity score but converges to some other balancing score. We call this property the balancing score property, and discuss a class of estimators that have this property. We introduce a targeted minimum loss-based estimator (TMLE) for a treatment-specific mean with the balancing score property that is additionally locally efficient and doubly robust. We investigate the new estimator’s performance relative to other estimators, including another TMLE, a propensity score matching estimator, an inverse probability of treatment weighted estimator, and a regression-based estimator in simulation studies.

Suggested Citation

  • Lendle Samuel David & Fireman Bruce & van der Laan Mark J., 2015. "Balancing Score Adjusted Targeted Minimum Loss-based Estimation," Journal of Causal Inference, De Gruyter, vol. 3(2), pages 139-155, September.
    • Handle: RePEc:bpj:causin:v:3:y:2015:i:2:p:139-155:n:1
    DOI: 10.1515/jci-2012-0012
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2012-0012
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2012-0012?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. repec:diw:diwwpp:dp485 is not listed on IDEAS
    2. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    3. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    4. Kosuke Imai & David A. van Dyk, 2004. "Causal Inference With General Treatment Regimes: Generalizing the Propensity Score," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 854-866, January.
    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. Wei Huang & Oliver Linton & Zheng Zhang, 2022. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1817-1830, October.
    2. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    3. Ugur, Mehmet & Trushin, Eshref, 2018. "Asymmetric information and heterogeneous effects of R&D subsidies: evidence on R&D investment and employment of R&D personel," Greenwich Papers in Political Economy 21943, University of Greenwich, Greenwich Political Economy Research Centre.
    4. Sánchez-Braza, Antonio & Pablo-Romero, María del P., 2014. "Evaluation of property tax bonus to promote solar thermal systems in Andalusia (Spain)," Energy Policy, Elsevier, vol. 67(C), pages 832-843.
    5. Guadalupe Serrano-Domingo & Francisco Requena-Silvente, 2013. "Examining the non-linear relationship between migration and trade," Working Papers 1310, Department of Applied Economics II, Universidad de Valencia.
    6. Li, Houjian & Zuo, Xingyi & Cao, Andi, 2024. "Empowering women brings change: The role of female cadres in enhancing elderly care public goods in Chinese villages," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 1063-1083.
    7. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Papers 2206.08503, arXiv.org, revised Jan 2025.
    8. Lee, Ji Yong & Nayga Jr, Rodolfo M. & Jo, Young & Restrepo, Brandon J., 2022. "Time use and eating patterns of SNAP participants over the benefit month," Food Policy, Elsevier, vol. 106(C).
    9. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    10. Cansino, José M. & Lopez-Melendo, Jaime & Pablo-Romero, María del P. & Sánchez-Braza, Antonio, 2013. "An economic evaluation of public programs for internationalization: The case of the Diagnostic program in Spain," Evaluation and Program Planning, Elsevier, vol. 41(C), pages 38-46.
    11. Tübbicke Stefan, 2022. "Entropy Balancing for Continuous Treatments," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 71-89, January.
    12. Gerhard Krug, 2017. "Augmenting propensity score equations to avoid misspecification bias – Evidence from a Monte Carlo simulation [Erweiterung der Propensity Score Gleichung zur Vermeidung von Fehlspezifikationen? Ein," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 205-231, December.
    13. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    14. Jones A.M & Rice N, 2009. "Econometric Evaluation of Health Policies," Health, Econometrics and Data Group (HEDG) Working Papers 09/09, HEDG, c/o Department of Economics, University of York.
    15. Hajime Seya & Takahiro Yoshida, 2017. "Propensity score matching for multiple treatment levels: A CODA-based contribution," Papers 1710.08558, arXiv.org.
    16. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    17. Suzuki, Aya & Mano, Yukichi & Abebe, Girum, 2018. "Earnings, savings, and job satisfaction in a labor-intensive export sector: Evidence from the cut flower industry in Ethiopia," World Development, Elsevier, vol. 110(C), pages 176-191.
    18. Alejo, Javier & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2018. "Quantile continuous treatment effects," Econometrics and Statistics, Elsevier, vol. 8(C), pages 13-36.
    19. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    20. Nguyen Viet, Cuong, 2012. "Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study," MPRA Paper 36377, University Library of Munich, Germany.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:causin:v:3:y:2015:i:2:p:139-155: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.degruyterbrill.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.