关键词: 因果关系, 因果关系分析, 因果关系预测, 概率失效, 政策干预

Abstract:

Causality prediction is an important part and a  promoing application of causality research. To simplify prediction, traditional methods usually use optimal structure equation or minimum feature set, such as Markov Blanket (MB) of the target variable, to make predictions. We present a novel method avoiding some limitations of the traditional methods by using global dynamic properties of causality analysis based on causal structure with samples to make prediction of the target variable under the effect of unknown policy manipulations performed by an external agent. Several experiments have been done to compare GC, VAR and several other popular methods with ICIC_Prediction based on four datasets published in challenge of “Causation and Prediction” in NIPS 2008 for analyzing and showing the advantages and properties of our method.

Key words: causality;causality analysis;causality prediction;probability failure;policy manipulation