# Markov equivalence of max-linear Bayesian networks

@inproceedings{Amendola2021MarkovEO, title={Markov equivalence of max-linear Bayesian networks}, author={Carlos Am'endola and Benjamin Hollering and Seth Sullivant and Ngoc Khue Tran}, booktitle={UAI}, year={2021} }

Max-linear Bayesian networks have emerged as highly applicable models for causal inference via extreme value data. However, conditional independence (CI) for max-linear Bayesian networks behaves differently than for classical Gaussian Bayesian networks. We establish the parallel between the two theories via tropicalization, and establish the surprising result that the Markov equivalence classes for max-linear Bayesian networks coincide with the ones obtained by regular CI. Our paper opens up…

## 2 Citations

The tropical geometry of causal inference for extremes

- Computer Science
- 2022

In this paper, intuition is given, backed by performances on benchmark datasets, for why and when causal inference for extremes should be employed over classical methods.

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