This interdisciplinary talk introduces the listeners to the power of Generative AI in the field of Causal Inference and its subsequent applications in Economics and Political Science. Our rigorous year-long research aims to develop a state-of-the-art Causal Inference technique: CausalGANs. Generative Adversarial Networks (GANs) is a popular  deep learning method which dominates the field of image generation. We harness the essence of GANs to create,  from scratch, a causal inference technique which modifies the architecture of GANs to solve the fundamental problem of “Missing Counterfactuals” in Causal Inference. In this thorough research, we set up a new framework, develop the notation, write mathematical proofs, and produce robust results by running over 200 parallelised experiments for each different set of parameters on High Power Computing.