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Causal additive models with smooth backfitting
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DOI：10.1515/jci-2024-0035.png)
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Abstract
En 中文
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.
Keywords:
smooth backfitting
causal discovery
additive models
nonparametric inference
structural equation models
Journal
J
IF:
1.8
Papers:
18
Citations:
0
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Cited Papers
FOUNDATIONS OF STRUCTURAL CAUSAL MODELS WITH CYCLES AND LATENT VARIABLES.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveCAM: CAUSAL ADDITIVE MODELS, HIGH-DIMENSIONAL ORDER SEARCH AND PENALIZED REGRESSIONANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveShareSaveShareSaveLEARNING HIGH-DIMENSIONAL DIRECTED ACYCLIC GRAPHS WITH LATENT AND SELECTION VARIABLESANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSave
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