Necessary and sufficient conditions for compatibility with memoryless sequential quantum processes combine directional conditional independence, pseudo-density matrix positivity, and a new algebraic consistency requirement.
Elements of Causal Inference: Foundations and Learning Algorithms , publisher =
2 Pith papers cite this work. Polarity classification is still indexing.
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CFM-SD treats physical simulators as do-operators to identify d-variable causal structures with O(d) interventions and reduces bias in molecular and battery prediction tasks.
citing papers explorer
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Causal-Order Identification of Memoryless Sequential Quantum Processes from Restricted Projective Data
Necessary and sufficient conditions for compatibility with memoryless sequential quantum processes combine directional conditional independence, pseudo-density matrix positivity, and a new algebraic consistency requirement.
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Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science
CFM-SD treats physical simulators as do-operators to identify d-variable causal structures with O(d) interventions and reduces bias in molecular and battery prediction tasks.