Digital Twin is a framework by which humans can interact with Internet of Things (IoT) systems in a natural way. The best way to think about Digital Twins is as the seat of Machine Learning and Artificial Intelligence in IoT systems. Our approach to Digital Twins is rooted in the firm conviction that Causality is paramount – knowing what underlying dynamics caused the surface IoT sensor measurements; this “inverse” problem of unearthing underlying causes from measurements is a hard “ill-posed” problem. We bring additional information in the form of Graph Causal Model simulation to constrain the inverse solution and make it tractable and meaningful. The Graph Causal Model is developed in collaboration with domain experts who from their vast experience can help identify a “chain” of causative factors. This information is used in a sophisticated solution involving Kalman Filter and neural network modified with the Graph Causal Model. End result is an Inverse Digital Twin that allows business owners to perform “what-if” analysis, assess counterfactual scenarios and prescribe actions that improve business performance.