Method, apparatus and system for estimating causality among observed variables

Abstract

Method, apparatus and system for estimating causality among observed variables are provided. 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 means of a mixed sparse causal inference, which is suitable for both continuous observed variables and discrete observed variables, using the mixed observed data under a constraint of directed acyclic graph, to estimate causality among the observed variables.

I/We claim: 
     
         1 . A method of causal relation analysis, comprising:
 obtaining observed data corresponding to a plurality of observed variables;   determining data types of the plurality of observed variables; and   determining, based on the data types, causal relations among the plurality of observed variables using a causal model that comprises a causality objective function for continuous observed variables and a causality objective function for discrete observed variables.   
     
     
         2 . The method of  claim 1 , wherein the plurality of observed variables comprise at least one of a continuous variable, a discrete variable, and combination thereof. 
     
     
         3 . The method of  claim 1  further comprising:
 determining the causal relation of one the plurality of observed variables with the other observed variables using the causality objective function for continuous observed variables if the one of the plurality of observed variables is a continuous variable; and 
 determining the causal relation of one the plurality of observed variables with the other observed variables using the causality objective function for discrete observed variables if the one of the plurality of observed variables is a discrete variable. 
 
     
     
         4 . The method of  claim 1 , wherein the causal model further comprises a parameter corresponding to the data type of the plurality of observed variables. 
     
     
         5 . The method of  claim 1 , wherein the causality objective function for continuous observed variables is built on an assumption that errors of observed variables comply with a Laplace distributions and a causality objective function for discrete observed variables is built on an assumption that errors of observed variables comply with a logistic distribution. 
     
     
         6 . The method of  claim 1 , wherein each of the causality objective function for continuous observed variables and the causality objective function for discrete observed variables is a likelihood function. 
     
     
         7 . The method of  claim 1 , wherein the causal relations are determined using the causal model under a constraint of directed acyclic graph and sparsity. 
     
     
         8 . The method of  claim 1 , wherein the causal model further comprises a parameter of a weighted factor for each of the plurality of observed variables, the weighted factor is to make the different observed variables comparable in terms of magnitude of the observed data. 
     
     
         9 . The method of  claim 8 , wherein the weighted factor is calculated on an assumption that errors of observed variables comply with a logistic distribution. 
     
     
         10 . The method of  claim 8 , wherein the weighted factor is calculated on an assumption that errors of observed variables comply with a Laplace distribution. 
     
     
         11 . The method of  claim 1 , wherein determining the causal relations further comprises determining an optimal causal order of the plurality of observed variables. 
     
     
         12 . The method of  claim 1  further comprising:
 transmit the causal relations among the plurality of observed variables. 
 
     
     
         13 . The method of  claim 1 , wherein the observed data comprises data related with at least one of pharmaceuticals, manufacturing, market, traffic, weather, and air quality. 
     
     
         14 . The method of  claim 1  further comprising:
 performing at least one of an analysis and prediction based on the causal relations and the observed variables. 
 
     
     
         15 . The method of  claim 1 , wherein at least one of the analysis and prediction comprises at least one: pharmaceuticals analysis, manufacturing analysis, market analysis, traffic forecast, weather forecast, and air quality forecast. 
     
     
         16 . An electronic device, comprising a processor configured to:
 obtain observed data corresponding to a plurality of observed variables;   determine data types of the plurality of observed variables; and   determine, based on the data types, causal relations among the plurality of observed variables using a causal model that comprises a causality objective function for continuous observed variables and a causality objective function for discrete observed variables.   
     
     
         17 . The electronic device of  claim 16 , wherein the plurality of observed variables comprise at least one of a continuous variable, a discrete variable, and combination thereof. 
     
     
         18 . The electronic device of  claim 16 , wherein the processor is further configured to:
 determine the causal relation of one the plurality of observed variables with the other observed variables using the causality objective function for continuous observed variables if the one of the plurality of observed variables is a continuous variable; and   determine the causal relation of one the plurality of observed variables with the other observed variables using the causality objective function for discrete observed variables if the one of the plurality of observed variables is a discrete variable.   
     
     
         19 . The electronic device of  claim 16 , wherein the causal model further comprises a parameter corresponding to the data type of the plurality of observed variables. 
     
     
         20 . The electronic device of  claim 16 , wherein the causality objective function for continuous observed variables is built on an assumption that errors of observed variables comply with a Laplace distributions and a causality objective function for discrete observed variables is built on an assumption that errors of observed variables comply with a logistic distribution.