Statistical and structural causal models (Pearl, Woodward) identify causes at whatever level of aggregation yields stable interventionist relationships, which may be supra-individual.

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Interventionist(philosophy of causation): A way of thinking about cause and effect based on what would happen if you actively changed or intervened in a situation, rather than just observing it passively.
Level of aggregation(describing where causes can be identified): The scale or scope at which you're looking at something—for example, individual people versus groups, or single events versus patterns across many events.
Pearl, Judea(referenced as a creator of causal models): A computer scientist and philosopher who developed modern methods for understanding causation mathematically, especially the idea that we can figure out causes by studying what happens when we change things on purpose.
Stable interventionist relationships(what causal models identify): Reliable cause-and-effect connections that stay the same when you deliberately change one thing and observe what happens to another.

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