Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference

In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway's length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method.