Most machine learning models are concerned with correlation. In contrast, Causal models are concerned with cause and effect relationships – for example – "How much would a power failure cost to a given manufacturing plant?" A structural causal model (SCM) represents causal dependencies using graphical models. Bayesian Networks are one of the most widely used SCMs. Bayesian Network consists of a DAG(Directed Acyclic Graph), a causal graph where nodes represent random variables and edges represent the relationship between them, and a conditional probability distribution (CPDs) associated with each of the random variables. Models can reflect both statistically significant information (learned from the data) and domain expertise simultaneously.

Most ML developers today are not familiar with causal models. Current ML models are based on co-relation. In contrast, causal models deal with cause and effect. Furthermore, correlation-based models have limited explainability, do not handle novel situations well, and need a lot more data. Causal models overcome many of these limitations.