A matrix algebra for graphical statistical models

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• Qingyuan Zhao (Statistical Laboratory)
• Friday 18 October 2024, 15:30-17:00
• MR12, Centre for Mathematical Sciences, Wilberforce Road, Cambridge.

If you have a question about this talk, please contact Martina Scauda.

Directed mixed graphs permit directed and bidirected edges between any two vertices. They were first considered in the path analysis developed by Sewall Wright and play an essential role in statistical modeling. We introduce a matrix algebra for walks on such graphs. Each element of the algebra is a matrix whose entries are sets of walks on the graph from the corresponding row to the corresponding column. The matrix algebra is then generated by applying addition (set union), multiplication (concatenation), and transpose to the two basic matrices consisting of directed and bidirected edges. We use it to formalize, in the context of Gaussian linear systems, the correspondence between important graphical concepts such as latent projection and graph separation with important probabilistic concepts such as marginalization and (conditional) independence. In two further examples regarding confounder adjustment and the augmentation criterion, we illustrate how the algebra allows us to visualize complex graphical proofs. A “dictionary” and LATE Xmacros for the matrix algebra are provided in the Appendix.

Preprint available: https://arxiv.org/pdf/2407.15744

This talk is part of the Causal Inference Reading Group series.

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