Causal Network Inference Based On Entropy

With the advancement of data science in recent years,a considerable amount of time-series data from many disciplines has been gathered,and the analysis and information mining of time-series data has become a hot issue.And,because of their capacity to eliminate confounding elements from data,causal inference approaches are gaining popularity.The following two parts of this study focus on the causal inference algorithm under the causal entropy optimum causal theory model,as well as the application of optimal causal inference to time-series data.Firstly,an optimal control result algorithm based on the proximity index is implemented in this thesis,based on the theory of target results and the optimal result range algorithm,to ensure that the object’s celestial events are within the proximity target range,ensuring that the equipment is fully utilized.The cause of the accident may be decreased during this period,which can be applied to the problem of network vulnerability exploitation.A community-based prediction model is provided by the network’s fundamental network community,which includes a basic model for computing models,a computing model for predicting network segments,a computing model for predicting network models,and a sub-model for network prediction.One or more sub-community prediction models are provided by a community.The traffic algorithm is calculated using KSG results,and the calculation results of the algorithm’s neighboring degree index are utilized in the calculation results of the probabilistic network parameters applied to the data set,resulting in a high level of accuracy and resilience.The impact of the setting of the neighbor parameter k on the inference outcomes is difficult to assess since the KSG causal entropy estimation technique requires a human hyperparame-ter setup.As a result,using kernel density estimation,this research provides a causal entropy estimate approach that minimizes the mean square integral error.It is proved that the opti-mal bandwidth selection scheme is a nonparametric and unbiased causal entropy estimation approach.To test the algorithm’s effectiveness,this paper generates sequence data using the structured causal model,then restores the graph structure of the structured causal model us-ing the KSG-based causal network inference method and the kernel density estimation-based causal network inference method,and finally obtains Conclusion.In terms of inference accu-racy,the kernel density estimation approach outperforms the KSG estimation method.Finally,we inferred the temporal causal network on the pigeon flock temporal data by using the method based on kernel density estimation,and obtained the conclusion that the causal structure of the pigeon flock switched frequently when the flock flew in a group by analyzing the temporal causal network structure.The research presented in this paper on entropy-based causal inference methods broadens the application scenario and scope of causal theory,enriches the theory of causal entropy es-timation,and provides a method for estimating causal entropy from unbiased samples,all of which are useful for the development and application of causal theory.