Collaborative Causal Discovery with Atomic Interventions
This work addresses the efficiency of causal discovery for multiple entities in scenarios like healthcare or social networks, offering a significant reduction in required interventions, though it is incremental in building on existing causal methods.
The paper tackles the problem of learning multiple causal graphs from independent entities with fewer interventions by introducing a collaborative causal discovery framework, achieving a reduction from roughly n to logarithmic in M interventions per entity when graphs are clustered.
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.