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Uncovering causal relationships through symmetries could revolutionize how we approach complex machine-learning tasks beyond IID constraints.
Causal effects between high-dimensional variables may be simpler than you think: they often depend only on low-dimensional summary statistics, or bottlenecks, of the causes.