Advanced as they may be, modern analytics systems fall short of enabling the complete digital transformation of manufacturing enterprises. ​ For example, instead of only detecting symptoms of impending machine failure, what would be more valuable would be to determine the actual cause of failure. ​ Causal Machine Learning, a recent advance in ML holds the problem to solve this problem. ​ To understand how it can be applied in Digital Twins to enable complete digital transformation for manufacturers, I had a conversation with Dr. PG Madhavan. ​ PG has deep expertise in Data Science and extensive experience in advanced analytics development, both in industry and academia. ​ Below is the outline of our conversation: ​ ✅ Enthusiasm about Digital Twins Today ✅ Why Predictive Maintenance is not the Killer App for IIoT ✅ What is the central purpose of a Digital Twin? ✅ Challenges in Integrating Digital Technologies for DT Realisation ✅ Role of Industrial IoT in Digital Twins ✅ Machine Learning Methods in Digital Twins ✅ Application of Root Cause Analytics Method in DTs ✅ Application of Causality in Industrial IoT Data ✅ Key Steps to Digital Transformation in Manufacturing ✅ Manufacturing Digital Transformation through Digital Twins ✅ PyWhy, an open-source repository of AWS & Microsoft joint work in Causality for machine learning. ✅ Systems Analytics Solutions