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Introduces Adversarial Causal Intervention Falsification (ACIF), a sequential game where a structural causal generator proposes observational and interventional distributions while an adversarial experimentalist selects interventions to falsify it. The paper provides theoretical guarantees, including finite-sample convergence and model-selection, bridging causal generative modeling, active discovery, and experimental design.
This paper introduces COAST, a causal-intelligence framework for designing constraint-aware interventions that drive complex systems between states, integrating causal discovery, modeling, and multi-objective optimization to identify minimal effective interventions with mechanistic rationales.