ICIC_Target: A Novel Discovery Algorithm for Local Causality Network of Target Variable

Causality research aims to reveal the law of evolution of nature, society and human. Nowadays, the causality research receives widespread attention for its important applications of human life and science research, but there are still many difficulties and challenges. This paper presents a unified model to explain the stimulating and inhibiting causalities. Based on this model, we also present a framework ICIC and a novel algorithm ICIC_Target to infer the local causal structure of a target variable from observational data without any limitation of some assumptions, such as assumption of acyclic structure, hidden variables and so on. Following our descriptions of causality essence and properties, as well as several classical theories proposed by Judea Pearl, Gregory F. Cooper and so on, we introduce concepts of exogenous variable and clique-like structure (IClique) to get rough ordering of variables, which are necessary for revealing the causality accurately and efficiently. To evaluate our approach, several experiments compared with HITON, IC, PC, PCMB and several methods based on four datasets with different data types have been done. The results demonstrate the higher performance and stronger robustness of our algorithm ICIC_Target. In this paper, we also discuss the advantages of stability and complexity of ICIC_Target.