IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i21p4506-d1272038.html
   My bibliography  Save this article

Exploratory Matching Model Search Algorithm (EMMSA) for Causal Analysis: Application to the Cardboard Industry

Author

Listed:
  • Richard Aviles-Lopez

    (Department of Computer Science and A.I., University of Granada, 18071 Granada, Spain)

  • Juan de Dios Luna del Castillo

    (Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain)

  • Miguel Ángel Montero-Alonso

    (Department of Statistics and Operational Research, University of Granada, 18071 Granada, Spain)

Abstract

This paper aims to present a methodology for the application of matching methods in industry to measure causal effect size. Matching methods allow us to obtain treatment and control samples with their covariates as similar as possible. The matching techniques used are nearest, optimal, full, coarsened exact matching (CEM), and genetic. These methods have been widely used in medical, psychological, and economic sciences. The proposed methodology provides two algorithms to execute these methods and to conduct an exhaustive search for the best models. It uses three conditions to ensure, as far as possible, the balance of all covariates, the maximum number of units in the treatment and control groups, and the most significant causal effect sizes. These techniques are applied in the carton board industry, where the causal variable is downtime, and the outcome variable is waste generated. A dataset from the carton board industry is used, and the results are contrasted with an expert in this process. Meta-analysis techniques are used to integrate the results of different comparative studies, which could help to determine and prioritize where to reduce waste. Two machines were found to generate more waste in terms of standardized measures whose values are 0.52 and 0.53, representing 48.60 and 36.79 linear meters (LM) on average for each production order with a total downtime of more than 3000 s. In general, for all machines, the maximum average wastage for each production order is 24.98 LM and its confidence interval is [13.40;36.23] LM. The main contribution of this work is the use of causal methodology to estimate the effect of downtime on waste in an industry. Particularly relevant is the contribution of an algorithm that aims to obtain the best matching model for this application. Its advantages and disadvantages are evaluated, and future areas of research are outlined. We believe that this methodology can be applied to other industries and fields of knowledge.

Suggested Citation

  • Richard Aviles-Lopez & Juan de Dios Luna del Castillo & Miguel Ángel Montero-Alonso, 2023. "Exploratory Matching Model Search Algorithm (EMMSA) for Causal Analysis: Application to the Cardboard Industry," Mathematics, MDPI, vol. 11(21), pages 1-34, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4506-:d:1272038
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/21/4506/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/21/4506/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben B. Hansen, 2008. "The prognostic analogue of the propensity score," Biometrika, Biometrika Trust, vol. 95(2), pages 481-488.
    2. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
    3. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
    4. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
    5. Rosenbaum, Paul R., 2010. "Design Sensitivity and Efficiency in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 692-702.
    6. Ben B. Hansen, 2004. "Full Matching in an Observational Study of Coaching for the SAT," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 609-618, January.
    7. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    8. Christopher Hagedorn & Johannes Huegle & Rainer Schlosser, 2022. "Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2027-2043, October.
    9. 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. Riccardo D'Alberto & Francesco Pagliacci & Matteo Zavalloni, 2023. "A socioeconomic impact assessment of three Italian national parks," Journal of Regional Science, Wiley Blackwell, vol. 63(1), pages 114-147, January.
    2. Andre Rossi Oliveira, 2024. "Evaluating the Short-term Causal Effect of Early Alert on Student Performance," Research in Higher Education, Springer;Association for Institutional Research, vol. 65(7), pages 1395-1419, November.
    3. España, F. & Arriagada, R. & Melo, O. & Foster, W., 2022. "Forest plantation subsidies: Impact evaluation of the Chilean case," Forest Policy and Economics, Elsevier, vol. 137(C).
    4. Zhexiao Lin & Peng Ding & Fang Han, 2023. "Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect," Econometrica, Econometric Society, vol. 91(6), pages 2187-2217, November.
    5. Adam C. Sales & Ben B. Hansen & Brian Rowan, 2018. "Rebar: Reinforcing a Matching Estimator With Predictions From High-Dimensional Covariates," Journal of Educational and Behavioral Statistics, , vol. 43(1), pages 3-31, February.
    6. Tenglong Li & Kenneth A. Frank & Mingming Chen, 2024. "A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study," Mathematics, MDPI, vol. 12(3), pages 1-14, January.
    7. Takasaki, Yoshito, 2020. "Impacts of disability on poverty: Quasi-experimental evidence from landmine amputees in Cambodia," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 85-107.
    8. Jennings, Wesley G. & Richards, Tara N. & Dwayne Smith, M. & Bjerregaard, Beth & Fogel, Sondra J., 2014. "A Critical Examination of the “White Victim Effect” and Death Penalty Decision-Making from a Propensity Score Matching Approach: The North Carolina Experience," Journal of Criminal Justice, Elsevier, vol. 42(5), pages 384-398.
    9. Vargas Da Cruz,Marcio Jose, 2014. "Do export promotion agencies promote new exporters ?," Policy Research Working Paper Series 7004, The World Bank.
    10. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
    11. Adeola Oyenubi & Martin Wittenberg, 2021. "Does the choice of balance-measure matter under genetic matching?," Empirical Economics, Springer, vol. 61(1), pages 489-502, July.
    12. Nicole E. Pashley & Luke W. Miratrix, 2021. "Insights on Variance Estimation for Blocked and Matched Pairs Designs," Journal of Educational and Behavioral Statistics, , vol. 46(3), pages 271-296, June.
    13. Yoshito Takasaki, 2019. "Disability and Poverty: Landmine Amputees in Cambodia," CIRJE F-Series CIRJE-F-1118, CIRJE, Faculty of Economics, University of Tokyo.
    14. Fukui Hideki, 2023. "Evaluating Different Covariate Balancing Methods: A Monte Carlo Simulation," Statistics, Politics and Policy, De Gruyter, vol. 14(2), pages 205-326, June.
    15. Waltl, Sofie R., 2018. "Estimating quantile-specific rental yields for residential housing in Sydney," Regional Science and Urban Economics, Elsevier, vol. 68(C), pages 204-225.
    16. Clara Bicalho & Adam Bouyamourn & Thad Dunning, 2022. "Conditional Balance Tests: Increasing Sensitivity and Specificity With Prognostic Covariates," Papers 2205.10478, arXiv.org.
    17. Pablo Geraldo Bast'ias, 2024. "Credible causal inference beyond toy models," Papers 2402.11659, arXiv.org.
    18. Stephen C. Nelson & Geoffrey P. R. Wallace, 2017. "Are IMF lending programs good or bad for democracy?," The Review of International Organizations, Springer, vol. 12(4), pages 523-558, December.
    19. Zhexiao Lin & Peng Ding & Fang Han, 2021. "Estimation based on nearest neighbor matching: from density ratio to average treatment effect," Papers 2112.13506, arXiv.org.
    20. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.

    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:gam:jmathe:v:11:y:2023:i:21:p:4506-:d:1272038. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.