Causal discovery from sequential data in ALS disease based on entropy criteria

One of the most important issues in predictive modeling is to determine major cause factors of a phenomenon and causal relationships between them. Extracting causal relationships between parameters in a natural phenomenon can be accomplished through checking the parameters' changes in consecutive events. In addition, using information and probabilistic theory help better conception of causal relationships of a phenomenon. Therefore, probabilistic causal discovery from sequential data of a natural phenomenon can be useful for dimension reduction and predicting the future trend of a process. In this paper, we introduce a novel method for causal discovery from a sequential data based on a probabilistic causal graph. In this method, first, Causal Feature Dependency matrix (CFD matrix) is generated based on the features' changes in consecutive events. Then, a probabilistic causal graph is created from CFD matrix. In this graph, some valueless features will be eliminated on the basis of entropy value of each conditional density function. Finally, prediction operation is performed based on the output of causal graph. Experimental results on the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) sequential data set from Amyotrophic Lateral Sclerosis (ALS) disease show that our proposed algorithm can predict the progression rate of ALS disease properly with high precision.