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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
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    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.
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