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The paper proposes bipartite graphical causal models (BGCMs) to resolve ambiguities in causal interventions for systems at equilibrium with cyclic dependencies, generalizing existing frameworks like causal Bayesian networks and structural causal models.
GenMatch is an end-to-end generative matching framework for micro-view order-dispatching in ride-hailing, addressing challenges like batch encoding and utility learning to improve dispatch quality, with demonstrated effectiveness in real-world tests.