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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.
This paper proposes a k-order relaxation of the faithfulness assumption for learning graphical Markov blankets, and introduces a proof-of-concept algorithm (kOMB) that can recover Markov blankets even under violations of faithfulness, such as parity-type relationships.
This paper provides a 211-page collection of pen-and-paper exercises covering key topics in machine learning, including linear algebra, optimisation, graphical models, and variational inference, intended as an educational resource.