Method, device and system for estimating causal relationship between observation variables

Abstract

A method, device, and system for estimating a causal relationship between observed variables are disclosed. According to the method disclosed by the invention, In response to receiving observations ofobserved variables, and the causal relationship target expression is determined based on the fitting inconsistency when the observation variables are used for fitting and the sparsity constraint on the causal network structure. Wherein the fitting inconsistency is adjusted based on a weighting factor of an observation variable, and the weighting factor of the observation variable represents the lower limit of the minimum cost required for fitting the target variable by using other observation variables except the observation variable. Then, through observation data and sparse causal reasoning, under the constraint of the directed acyclic graph, the causal relationship target expression is subjected to optimal solution, so that the causal relationship among multiple observation variables is estimated. By utilizing the method and the device, the sensitivity caused by an observation variable estimation error can be reduced, and a more accurate causal relationship can be obtained.

Description

technical field [0001] The present disclosure relates to the technical field of data mining, and more particularly to a method, device and system for estimating the causal relationship between observed variables. Background technique [0002] In the era of big data, a large amount of data can be obtained through various data collection channels. Through data analysis and mining of these data, a lot of useful information can be obtained. However, in many application fields, people often only see the appearance of the system, but cannot gain insight into the complex mechanism and process behind the system, but can only gain an empirical understanding. [0003] The causal structure learning is dedicated to the observation data based on the system, automatically restores the complex mechanism behind the system, and restores the data generation process. At present, causal structure learning technology has been applied in many fields such as pharmaceuticals, manufacturing, and m...

AI Technical Summary

Problems solved by technology

[0009] However, the existing causal modeling methods are very sensitive to the estimation errors of different observed variables. When there are large differences in the magnitude of observed variables or errors in the estimation of variables, the accuracy of causal structure learning will drop significantly.

In addition, the existing reasoning algorithms have high time complexity and cannot support the learning of complex causal structures with high dimensions of observed variables.

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