Causal Discovery Algorithm Based on Non-stationary Additive Noise Model
Causal discovery aims to mine the causal relationship between variables through observed data.Most existing methods assume that the data-generation process is stationary.However,this assumption is often not satisfied in the application environments,leading to unreliable results.This study reveals that non-stationary disturbances in some scenes are highly correlated with time-series information.Therefore,based on the additive noise model,the method portrays non-stationary disturbances as a mapping of time series information and proposes a non-stationary additive noise model and its identification conditions.This study proposes a two-stage causality discovery algorithm based on identification conditions.Specifically,residuals are obtained through regression analysis and are used to evaluate the independence of selecting a leaf node in the initial phase of the algorithm.The causal order of the observed variable sets is thereafter obtained iteratively until all the variables have been included.In the second phase of the algorithm,regression analysis and independence tests are performed again to eliminate redundant causal relationships identified in the first stage,which helps to obtain the final causal structure of the observed variable set.Experimental results demonstrate that the proposed algorithm outperforms other algorithms such as Constraint-based causal Discovery heterogeneous/Non-stationary Data(CD-NOD),LPCMCI,and TiMINo.For the synthetic datasets,the proposed algorithm achieves an average Fl value of 0.85.In real-world structural datasets,the F1 value of the proposed algorithm increases by an average of 41.12％,signifying that the algorithm can learn more information about the causal structure from a dataset of non-stationary variables.
causal discoverycausal structurenon-stationary disturbancesadditive noise modelfunctional causal model
万方数据
维普
