Nec Corporation

Wenjuan Wei

Chunchen Liu

Lu Feng

Zheng Pan

Lvye Cui

Implementations of the present disclosure relate to a method, system and program product for determining a causality between a plurality of variables. A method is provided for determining a causality between a plurality of vectors, which comprises: in response to a data set of a plurality of samples associated with the plurality of variables being collected, obtaining a matrix describing the causality between the plurality of variables, each sample among the plurality of samples comprising data that corresponds to the plurality of variables; determining a fitness degree and an expert knowledge constraint which are associated with the causality based on the data set and the matrix, wherein the expert knowledge constraint comprises at least one of an edge constraint on a direct causality between two variables in the matrix and a path constraint on an indirect causality between two variables in the matrix; building a problem formula describing the causality according to the determined fitness degree and expert knowledge constraint; and solving the built problem formula so as to obtain a candidate result of the matrix. Further, there is provided a corresponding system and computer program product.

Method, system, and computer program product for determining causality

A method, device and system for estimating causality among observed variables are provided. In response to receiving observation data of a plurality of observed variables, a causality objective function is determined, based on fitting inconsistencies when fitting is performed using the observed variables and a sparse constraint for a causal network structure. The fitting inconsistencies are adjusted based on weighting factors of the observed variables, wherein a weighting factor of an observed variable indicates a minimum underestimate value of cost required for fitting a target variable using all other observed variables than the above observed variable. Then, the causality among the plurality of observed variables is estimated by using the observations data to optimally solve the causality objective function through sparse causal reasoning under a directed acyclic graph constraint.

Method, device and system for estimating causality among observed variables

The present disclosure relates to a method, apparatus and system for determining accessible paths between two nodes. According to the method of the present disclosure, an adjacency matrix of a graph, which includes a plurality of points and edges connecting the plurality of points, is first established; then, search is made for an accessible path that can reach a destination from a starting point based on the adjacency matrix. Especially, a termination condition for the searching is set based on an already-sought path during the searching, and the corresponding search is terminated in response to the termination condition being met during the searching. With the present disclosure, it may substantially reduce repeated traversal, obvious reduce the time taken to search for accessible paths between two nodes, and significantly improve the efficiency in searching for the accessible path.

Method, apparatus and system for determining accessible paths between two nodes

Implementations of the present disclosure relate to a method, system and program product for determining a causality between a plurality of variables. Specifically, a method is provided for determining a causality between a plurality of vectors, which comprises: in response to a data set of a plurality of samples associated with the plurality of variables being collected, obtaining a matrix describing the causality between the plurality of variables, each sample among the plurality of samples comprising data that corresponds to the plurality of variables; determining fitness associated with the causality and sparsity associated with the causality based on the data set and the matrix, wherein the sparsity comprises a constraint on each vector among a plurality of vectors in the matrix, the constraint corresponding to a variable among the plurality of variables; building a first problem formula describing the causality according to the determined fitness and sparsity; and solving the built first problem formula so as to obtain a candidate result of the matrix. Further, there is provided a corresponding system and computer program product.

Embodiments of the present disclosure relate to a method, a device and a computer-readable storage medium for data processing. The method for data processing comprises: obtaining a set of observation samples regarding a plurality of factors, one of the set of observation samples comprising respective observed values of the plurality of factors. The method further comprises: estimating, for each of the plurality of factors and based on the set of observation samples, a distribution that differences between observed values of the factor and estimated values of the factor follow. The method further comprises determining, based at least on the estimated distribution, a causal structure representing a causal relationship among the plurality of factors. Embodiments of the present disclosure further provide a device and a computer-readable storage medium for implementing the above method. The embodiments of the present disclosure can accurately and robustly discover the causal relationship among a plurality of factors without making any assumptions about the relationship between the data distribution and the factors, and affect the observed value of the target factor based on the causal relationship.

Method, device, and medium for data processing

Embodiments of the present disclosure provide a data processing method, an electronic device and a computer-readable storage medium. The data processing method comprises: obtaining a model representing causal relations among a plurality of variables based on a set of observation data of the plurality of variables; determining, based on the obtained model, a first and a second variables having direct causal relation in the plurality of variables; determining whether the first and second variables are independent from each other; and in response to the first and second variables being independent from each other, deleting the direct causal relation between the first and second variables from the obtained model. With the data processing method of the present disclosure, pseudo-causes can be removed effectively so that causal relations among a plurality of variables can be represented more accurately.

Data processing method and electronic device

In response to receiving observed data of mixed observed variables, a mixed causality objective function, being suitable for continuous observed variables and discrete observed variables is determined, wherein the mixed causality objective function includes a causality objective function for continuous observed variables and a causality objective function for discrete observed variables and the fitting inconsistency is adjusted based on weighted factors of the observed variables. Then, the mixed causality objective function is optimally solved by using a mixed sparse causal inference, suitable for both continuous observed variables and discrete observed variables, using the mixed observed data under a constraint of a directed acyclic graph, to estimate causality among the observed variables.

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Beijing, China

Wenjuan Wei