1 citations · 3 across the 9 of their papers we have counts for
9 papers · 1 filter
Spatiotemporally Consistent Multivariate Bias Correction for Climate Projections via Nested Vine Copulas
Theresa Meier, Erwan Koch, Valérie Chavez-Demoulin +1
Climate models are essential for understanding large-scale climate dynamics and long-term climate change, yet they exhibit systematic biases when compared with historical observati…
Causal Discovery in Multivariate Extremes with a Hydrological Analysis of Swiss River Discharges
Linda Mhalla, Valérie Chavez-Demoulin, Philippe Naveau
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 compa…
Causality and extremes
Valérie Chavez-Demoulin, Linda Mhalla
In this work, we summarize the state-of-the-art methods in causal inference for extremes. In a non-exhaustive way, we start by describing an extremal approach to quantile treatment…
Structural restrictions in local causal discovery: identifying direct causes of a target variable
Juraj Bodik, Valérie Chavez-Demoulin
We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs (DAGs) that represent the…
Identifiability of causal graphs under nonadditive conditionally parametric causal models
Juraj Bodik, Valérie Chavez-Demoulin
Existing approaches to causal discovery often rely on restrictive modeling assumptions that limit their applicability in real-world settings, particularly when data are heavy-taile…
A competing risks interpretation of Hawkes processes
Maximilian Aigner, Valérie Chavez-Demoulin
We give a construction of the Hawkes process as a piecewise competing risks model. We argue that the most natural interpretation of the self-excitation kernel is the hazard functio…