Combining Structure Equation Model with Bayesian Networks for predicting with high accuracy of recommending surgery for better survival in Benign prostatic hyperplasia patients

Abstract: Causal discovery is the key aspect of science. Inferring causality can be achieved in various ways. Typically, you start with your hypothesis (based on what you know so far) and based on the data you collect, you update your hypothesis. In a nutshell, causality can be inferred via your background knowledge and empirical data. Causal Both Bayesian networks (BN) and Structural equation model (SEM) are graphical models that are able to model causality both from background knowledge and empirical data. SEM heavily relies on your background knowledge and use data to justify your knowledge. On the other hand, BN can combine your background knowledge with a causal model that gives the maximum likelihood based on data. If the relationships turn out to be statistically significant, then expert’s knowledge is considered statistically valid and can be used to provide guidelines in practice using the model based on expert’s knowledge.