1,588 research outputs found
Discovering Mixtures of Structural Causal Models from Time Series Data
In fields such as finance, climate science, and neuroscience, inferring
causal relationships from time series data poses a formidable challenge. While
contemporary techniques can handle nonlinear relationships between variables
and flexible noise distributions, they rely on the simplifying assumption that
data originates from the same underlying causal model. In this work, we relax
this assumption and perform causal discovery from time series data originating
from mixtures of different causal models. We infer both the underlying
structural causal models and the posterior probability for each sample
belonging to a specific mixture component. Our approach employs an end-to-end
training process that maximizes an evidence-lower bound for data likelihood.
Through extensive experimentation on both synthetic and real-world datasets, we
demonstrate that our method surpasses state-of-the-art benchmarks in causal
discovery tasks, particularly when the data emanates from diverse underlying
causal graphs. Theoretically, we prove the identifiability of such a model
under some mild assumptions
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
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