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The tropical geometry of causal inference for extremes

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arxiv 2207.10227 v1 pith:OU6A6NJD submitted 2022-07-20 math.ST math.COstat.TH

The tropical geometry of causal inference for extremes

classification math.ST math.COstat.TH
keywords geometrytropicalcausalclassicalinferencestatisticsanaloguebenchmark
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Extreme value statistics is the max analogue of classical statistics, while tropical geometry is the max analogue of classical geometry. In this paper, we review recent work where insights from tropical geometry were used to develop new, efficient learning algorithms with leading performance on benchmark datasets in extreme value statistics. We give intuition, backed by performances on benchmark datasets, for why and when causal inference for extremes should be employed over classical methods. Finally, we list some open problems at the intersection of causal inference, tropical geometry and deep learning.

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    For a noiseless Gaussian query-value multiple-instance model, a single random assignment yields a value-vector estimate aligned with the truth when the number of bags is at least of order d n squared (ln n) to the sixth.