  Identifying the causal variables of an environment and how to intervene on
them is of core value in applications such as robotics and embodied AI. While
an agent can commonly interact with the environment and may implicitly perturb
the behavior of some of these causal variables, often the targets it affects
remain unknown. In this paper, we show that causal variables can still be
identified for many common setups, e.g., additive Gaussian noise models, if the
agent's interactions with a causal variable can be described by an unknown
binary variable. This happens when each causal variable has two different
mechanisms, e.g., an observational and an interventional one. Using this
identifiability result, we propose BISCUIT, a method for simultaneously
learning causal variables and their corresponding binary interaction variables.
On three robotic-inspired datasets, BISCUIT accurately identifies causal
variables and can even be scaled to complex, realistic environments for
embodied AI.
