  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.
