A recursive causal inference method based on causal segmentation

Abstract

The invention belongs to the technical field of data mining, in particular to a recursive causal inference method based on causal segmentation. The method of the invention adopts the divide-and-conquer strategy, recursively utilizes the low-order conditional independence test to divide the data set into layers of causality, then reconstructs the causality of each sub-data set, and finally merges to obtain the whole causality information of the data set. This method can be used in high-dimensional data set for causal inference and causal relationship mining. Under the background of big data era, causality inference algorithm has been widely used in the fields of economics, Internet social network, medical big data and so on, but high-dimensional data problem is a common problem in the industry information intelligence, so it is urgent to solve the related problems in this field. The present invention is helpful to solve the problem of how to deal with the ever-increasing mass data causal information mining, and plays an important role in extracting the precious causal information in the mass data.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a causal network construction method suitable for modeling biological information, financial networks and social networks. Background technique [0002] With the advent of the era of big data, causality inference algorithm technology has been widely used in the fields of economics, Internet social networks, and medical big data. With the growing mass of data and the trend of high-dimensional and complex data structures, the problem of causality inference in dealing with high-dimensional data has attracted great attention from experts and scholars at home and abroad. The problem of high-dimensional data is a common problem encountered in the intelligentization of industry information. It is imminent to solve related problems in this field, and it has become a research hotspot in the field of machine learning. [0003] Recently, some studies have used the character...

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Problems solved by technology

However, the accuracy of these methods is limited by the size of the data set. When the variable size in the data set is large (about 100~200), due to the low accuracy of the high-order conditional independence (CI) test and the high time complexity These methods cannot make a more accurate judgment on the causal relationship between the data, which leads to the failure of mining or regulation of the entire causal system.

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