Causal Discovery in Multivariate Extremes with a Hydrological Analysis of Swiss River Discharges
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913359446671360 |
|---|---|
| author | Mhalla, Linda Chavez-Demoulin, Valérie Naveau, Philippe |
| author_facet | Mhalla, Linda Chavez-Demoulin, Valérie Naveau, Philippe |
| contents | Causal asymmetry is based on the principle that an event is a cause only if its absence would not have been a cause. From there, uncovering causal effects becomes a matter of comparing a well-defined score in both directions. Motivated by studying causal effects at extreme levels of a multivariate random vector, we propose to construct a model-agnostic causal score relying solely on the assumption of the existence of a max-domain of attraction. Based on a representation of a Generalized Pareto random vector, we construct the causal score as the Wasserstein distance between the margins and a well-specified random variable. The proposed methodology is illustrated on a hydrologically simulated dataset of different characteristics of catchments in Switzerland: discharge, precipitation, and snowmelt. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10371 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Discovery in Multivariate Extremes with a Hydrological Analysis of Swiss River Discharges Mhalla, Linda Chavez-Demoulin, Valérie Naveau, Philippe Methodology Applications Causal asymmetry is based on the principle that an event is a cause only if its absence would not have been a cause. From there, uncovering causal effects becomes a matter of comparing a well-defined score in both directions. Motivated by studying causal effects at extreme levels of a multivariate random vector, we propose to construct a model-agnostic causal score relying solely on the assumption of the existence of a max-domain of attraction. Based on a representation of a Generalized Pareto random vector, we construct the causal score as the Wasserstein distance between the margins and a well-specified random variable. The proposed methodology is illustrated on a hydrologically simulated dataset of different characteristics of catchments in Switzerland: discharge, precipitation, and snowmelt. |
| title | Causal Discovery in Multivariate Extremes with a Hydrological Analysis of Swiss River Discharges |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2405.10371 |