Inequality Constraints in Directed Acyclic Graphs with Hidden Variables

Directed acyclic graphs (DAGs) with hidden variables are often used to characterize causal relations between variables in a system. When some variables are unobserved, DAGs imply a notoriously complicated set of constraints on the distribution of observed variables. I will discuss how to construct inequality constraints implied by graphical criterions that can be applied to hidden variable DAGs and learn from them about the true causal model. The focus of my presentation will be on causal models that exhibit d-separation relations and with a promise that unobserved variables have known cardinalities (arXiv:2109.05656), and on causal models that exhibit e-separation relations (arXiv:2107.07087). In addition, I will discuss the relationship between causal inference and quantum foundations, and the possibility of leveraging our results to study causal influence in models that involve quantum systems.

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Zoom meeting details

Topic: Quantum Information and Quantum Computing Working Group Seminar
Time: Wednesday, 02.02.2022, 15:15 Warsaw (CET)

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