Method, apparatus and system for estimating causality among observed variables

An apparatus for estimating causality among observed variables, comprising:

• a mixed objective function determination module configured to, in response to receiving observed data of mixed observed variables, determine a mixed causality objective function suitable for continuous observed variables and discrete observed variables, based on fitting inconsistency upon fitting with the observed variables and sparsity constraints on a causal network structure for the observed variables, wherein the mixed causality objective function includes a causality objective function for continuous observed variables and a causality objective function for discrete observed variables, wherein the fitting inconsistency is adjusted based on weighted factors of the observed variables, a weighted factor of an observed variables represents a lower limit of a minimum cost required for fitting an object variable using other observed variables than the observed variable, and weighted factors for the discrete observed variables are different from weighted factors for the continuous observed variables;
• and
• a mixed causal inference module configured to optimally solve a mixed causality objective function for the observed variables by means of a mixed sparse causal inference, which is suitable for both continuous observed variables and discrete observed variables, using the observed data under a constraint of directed acyclic graph, to estimate causality among the observed variables.