Causal inference, a cornerstone in disciplines such as economics, genomics, and medicine, is increasingly being recognized as fundamental to advancing the field of robotics. In particular, the ability to reason about cause and effect from observational data is crucial for robust generalization in robotic systems. However, the construction of a causal graphical model, a mechanism for representing causal relations, presents an immense challenge. Currently, a nuanced grasp of causal inference, coupled with an understanding of causal relationships, must be manually programmed into a causal graphical model. To address this difficulty, we present initial results towards a human-centered augmented reality framework for creating causal graphical models. Concretely, our system bootstraps the causal discovery process by involving humans in selecting variables, establishing relationships, performing interventions, generating counterfactual explanations, and evaluating the resulting causal graph at every step. We highlight the potential of our framework via a physical robot manipulator on a pick-and-place task.