Paper overviews importance of Causality in AI and highlights important aspects of it. Current state of AI deals with only association/curve fitting of data without need of a model. But this is far from human-like intelligence who have a mental representation that is manipulated from time-to-time using data and queried with What If? questions. To incorporate this, one needs to add two more layers on top of curve fitting module which are interventions(What if I do this?) and counterfactuals(What if I had done this?). Interventions are represented by P(y|do(x)) where do(x) is action 'x' performed leading to change in behavior of certain variables, thereby making previous data useless for its estimation. Counterfactuals are represented by P(y(x)|x',y') where x',y' are observed and goal is to determine probability of y given x. Pearl suggests use of Structural Causal Models(SCM) for interventions and counterfactuals. SCM takes a query(association, intervention or counterfactual) and graphical model(based on assumptions) to build a estimand(mathematical recipe). Estimand takes data and produces an estimate(answer) with confidence. Assumptions are fine tuned based on data. There are lot of advantages provided by Causal Models - (1)Graphical models make it easier to read the assumptions, thereby providing transparency. It also makes it easier to verify all dependencies encoded in data with the help of d-separation, thereby providing testability (2)Causal models help in mediation analysis that identify mechanisms that change cause to effect for explainability (3)Current transfer learning approaches are tried at association level but it cannot identify mechanisms that are affected by changes (4)Causality provides tools to recover causal relationships when data has missing attributes unlike statistical analysis that provide tools only when values are missing at random i.e. independent of other variables.