IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v13y2025i1p37n1001.html

Causal additive models with smooth backfitting

Author

Listed:
  • Morville Asger B.

    (Department of Statistics, Seoul National University, Seoul, South Korea)

  • Park Byeong U.

    (Department of Statistics, Seoul National University, Seoul, South Korea)

Abstract

A fully nonparametric approach to learning causal structures from observational data is proposed. The method is described in the setting of additive structural equation models with a link to causal inference. The estimation procedure of the additive structural equation functions is based on a novel application of the smooth backfitting (SBF) approach. The flexibility of the nonparametric procedure results in strong theoretical properties in the estimation of the variable ordering. It is shown that under mild conditions, the ordering estimate is consistent. Through simulations, it is demonstrated that our method is superior to the state-of-the-art approaches to causal learning. In particular, the SBF approach shows robustness when the noise is heteroscedastic.

Suggested Citation

  • Morville Asger B. & Park Byeong U., 2025. "Causal additive models with smooth backfitting," Journal of Causal Inference, De Gruyter, vol. 13(1), pages 1-37.
    • Handle: RePEc:bpj:causin:v:13:y:2025:i:1:p:37:n:1001
    DOI: 10.1515/jci-2024-0035
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2024-0035
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2024-0035?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. Oliver Linton & E. Mammen & J. Nielsen, 1997. "The Existence and Asymptotic Properties of a Backfitting Projection Algorithm Under Weak Conditions," Cowles Foundation Discussion Papers 1160, Cowles Foundation for Research in Economics, Yale University.
    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. Lee, Kyeongeun & Lee, Young K. & Park, Byeong U. & Yang, Seong J., 2018. "Time-dynamic varying coefficient models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 50-65.
    2. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers CWP20/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. repec:hum:wpaper:sfb649dp2012-042 is not listed on IDEAS
    4. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    5. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    6. Suneel Babu Chatla, 2023. "Nonparametric inference for additive models estimated via simplified smooth backfitting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 71-97, February.
    7. Degui Li & Oliver Linton & Zudi Lu, 2012. "A flexible semiparametric model for time series," CeMMAP working papers 28/12, Institute for Fiscal Studies.
    8. Lin, Lu & Song, Yunquan & Liu, Zhao, 2014. "Local linear–additive estimation for multiple nonparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 252-269.
    9. Gayle, Wayne-Roy & Namoro, Soiliou Daw, 2013. "Estimation of a nonlinear panel data model with semiparametric individual effects," Journal of Econometrics, Elsevier, vol. 175(1), pages 46-59.
    10. repec:hum:wpaper:sfb649dp2010-059 is not listed on IDEAS
    11. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    12. Gao, Jiti & Lu, Zudi & Tjostheim, Dag, 2003. "Estimation in semiparametric spatial regression," MPRA Paper 11979, University Library of Munich, Germany, revised Jul 2005.
    13. Guo, Zheng-Feng & Shintani, Mototsugu, 2011. "Nonparametric lag selection for nonlinear additive autoregressive models," Economics Letters, Elsevier, vol. 111(2), pages 131-134, May.
    14. Fei Liu & Jiti Gao & Yanrong Yang, 2020. "Time-Varying Panel Data Models with an Additive Factor Structure," Monash Econometrics and Business Statistics Working Papers 42/20, Monash University, Department of Econometrics and Business Statistics.
    15. Vanhems, Anne & Van Keilegom, Ingrid, 2011. "Semiparametric transformation model with endogeneity: a control function approach," LIDAM Discussion Papers ISBA 2011011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. O. Linton & E. Mammen, 2005. "Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods," Econometrica, Econometric Society, vol. 73(3), pages 771-836, May.
    17. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers 20/12, Institute for Fiscal Studies.
    18. Vanhems, Anne & Van Keilegom, Ingrid, 2019. "Estimation Of A Semiparametric Transformation Model In The Presence Of Endogeneity," Econometric Theory, Cambridge University Press, vol. 35(1), pages 73-110, February.
    19. Gutknecht, Daniel, "undated". "Do Reservation Wages Decline Monotonically? A Novel Statistical Test," Economic Research Papers 270635, University of Warwick - Department of Economics.
    20. Li, Degui & Linton, Oliver & Lu, Zudi, 2015. "A flexible semiparametric forecasting model for time series," Journal of Econometrics, Elsevier, vol. 187(1), pages 345-357.
    21. Suphi Sen & Bertrand Melenberg & Herman R. J. Vollebergh, 2016. "Identification and Estimation of the Environmental Kuznets Curve: Pairwise Differencing to Deal with Nonlinearity and Nonstationarity," CESifo Working Paper Series 5837, CESifo.
    22. Berthold R. Haag, 2008. "Non‐parametric Regression Tests Using Dimension Reduction Techniques," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 719-738, December.

    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:13:y:2025:i:1:p:37:n:1001. 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.