  We introduce a new Collaborative Causal Discovery problem, through which we
model a common scenario in which we have multiple independent entities each
with their own causal graph, and the goal is to simultaneously learn all these
causal graphs. We study this problem without the causal sufficiency assumption,
using Maximal Ancestral Graphs (MAG) to model the causal graphs, and assuming
that we have the ability to actively perform independent single vertex (or
atomic) interventions on the entities. If the $M$ underlying (unknown) causal
graphs of the entities satisfy a natural notion of clustering, we give
algorithms that leverage this property and recovers all the causal graphs using
roughly logarithmic in $M$ number of atomic interventions per entity. These are
significantly fewer than $n$ atomic interventions per entity required to learn
each causal graph separately, where $n$ is the number of observable nodes in
the causal graph. We complement our results with a lower bound and discuss
various extensions of our collaborative setting.
