IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v5y2017i2p15n6.html

Counterfactual-Based Prevented and Preventable Proportions

Author

Listed:
  • Yamada Kentaro

    (Department of Statistical Science, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan)

  • Kuroki Manabu

    (Graduate School of Engineering, Yokohama National University, 79-1 Tokiwadai, Hodogaya-ku, Yokohama, 240-8501, Japan)

Abstract

Prevented and preventable fractions have been widely used in medical science to evaluate the proportion of new diseases that can be averted by a protective exposure. However, most existing formulas used in practical situations cannot be interpreted as proportions without any further assumptions because they are obtained according to different target populations and may fall outside the range [0.000,1.000]$[0.000,1.000]$. To solve this problem, this paper proposes counterfactual-based prevented and preventable proportions. When both causal effects and observed probabilities are available, we show that the proposed measures are identifiable under the negative monotonicity assumption. Additionally, when the negative monotonicity assumption is violated, we formulate the bounds on the proposed measures. We also show that negative monotonicity together with exogeneity induces equivalence between the proposed measures and existing measures.

Suggested Citation

  • Yamada Kentaro & Kuroki Manabu, 2017. "Counterfactual-Based Prevented and Preventable Proportions," Journal of Causal Inference, De Gruyter, vol. 5(2), pages 1-15, September.
    • Handle: RePEc:bpj:causin:v:5:y:2017:i:2:p:15:n:6
    DOI: 10.1515/jci-2016-0020
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2016-0020
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2016-0020?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. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, 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. Sven Resnjanskij & Jens Ruhose & Simon Wiederhold & Ludger Wößmann, 2021. "Mentoring verbessert die Arbeitsmarktchancen von stark benachteiligten Jugendlichen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 74(02), pages 31-38, February.
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    4. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    5. Bonesrønning, Hans & Finseraas, Henning & Hardoy, Ines & Iversen, Jon Marius Vaag & Nyhus, Ole Henning & Opheim, Vibeke & Salvanes, Kari Vea & Sandsør, Astrid Marie Jorde & Schøne, Pål, 2022. "Small-group instruction to improve student performance in mathematics in early grades: Results from a randomized field experiment," Journal of Public Economics, Elsevier, vol. 216(C).
    6. Peydró, José-Luis & Jiménez, Gabriel & Kenan, Huremovic & Moral-Benito, Enrique & Vega-Redondo, Fernando, 2020. "Production and financial networks in interplay: Crisis evidence from supplier-customer and credit registers," CEPR Discussion Papers 15277, C.E.P.R. Discussion Papers.
    7. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
    8. Hairu Wang & Yukun Liu & Haiying Zhou, 2025. "Score test for unconfoundedness under a logistic treatment assignment model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(4), pages 517-533, August.
    9. Satarupa Bhattacharjee & Bing Li & Xiao Wu & Lingzhou Xue, 2025. "Doubly robust estimation of causal effects for random object outcomes with continuous treatments," Papers 2506.22754, arXiv.org.
    10. Konrad Menzel, 2021. "Structural Sieves," Papers 2112.01377, arXiv.org, revised Apr 2022.
    11. Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2020. "Sampling‐Based versus Design‐Based Uncertainty in Regression Analysis," Econometrica, Econometric Society, vol. 88(1), pages 265-296, January.
    12. Andrés Elberg & Pedro M. Gardete & Rosario Macera & Carlos Noton, 2019. "Dynamic effects of price promotions: field evidence, consumer search, and supply-side implications," Quantitative Marketing and Economics (QME), Springer, vol. 17(1), pages 1-58, March.
    13. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    14. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    15. Jeon, Sung-Hee & Pohl, R. Vincent, 2019. "Medical innovation, education, and labor market outcomes of cancer patients," Journal of Health Economics, Elsevier, vol. 68(C).
    16. Johnsen, Åshild A. & Kvaløy, Ola, 2021. "Conspiracy against the public - An experiment on collusion11“People of the same trade seldom meet together, even for merriment and diversion, but the conversation ends in a conspiracy against the publ," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    17. Pedro Carneiro & Sokbae Lee & Daniel Wilhelm, 2020. "Optimal data collection for randomized control trials," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 1-31.
    18. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    19. Steffen Andersen & Philippe d'Astous & Jimmy Martínez-Correa & Stephen H. Shore, 2018. "Responses to Savings Commitments: Evidence from Mortgage Run-offs," Cahiers de recherche / Working Papers 1, Institut sur la retraite et l'épargne / Retirement and Savings Institute.
    20. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.

    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:5:y:2017:i:2:p:15:n:6. 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.