  We introduce a novel framework to identify perception-action loops (PALOs)
directly from data based on the principles of computational mechanics. Our
approach is based on the notion of causal blanket, which captures sensory and
active variables as dynamical sufficient statistics -- i.e. as the "differences
that make a difference." Moreover, our theory provides a broadly applicable
procedure to construct PALOs that requires neither a steady-state nor Markovian
dynamics. Using our theory, we show that every bipartite stochastic process has
a causal blanket, but the extent to which this leads to an effective PALO
formulation varies depending on the integrated information of the bipartition.
