Connectionists: [Deadline Extension] [CFP] NeurIPS 2022 workshop "A causal view on dynamical systems" - deadline extended: Sept 26, 23:59 AoE

[NeurIPS 2022 Causal Dynamics workshop]

Apologies for cross-posting, we have extended the submission deadline for
our workshop:First Workshop "A causal view on dynamical systems" at NeurIPS
2022: https://sites.google.com/view/caudyn2022, December 3, 2022, New
Orleans, USA

Submission deadline: *September 26, 2022 *11:59 pm AoE

Topic and motivation

Multivariate time series are a common data modality in many scientific
fields, such as physics, genomics, neuroscience or economics. A major goal
in these disciplines is to answer causal questions, such as gene regulatory
network inference in biology or identifying causal drivers of extreme
(compound) weather events in climate science, which requires the inference
of causal relationships in dynamical systems. Similar to the static
setting, accessibility, feasibility, and ethical considerations complicate
direct experimentation and measurement of such relations in time-dependent
settings. While certain properties of time series may aid practical causal
inference, such as the arrow of time, most identifiability results for
causal discovery as well as cause effect estimation are restricted to the
static setting (as in structural causal models or potential outcomes).
These results typically do not extend to temporally evolving dynamics.
Consequently, natural sciences mostly resort to continuous-time dynamical
systems such as ODEs and PDEs to describe hypothesized mechanisms
underlying the data generating processes. While such models also allow for
causal interpretations as well as predictions under hypothetical
interventions, data-driven discovery of continuous-time dynamical systems
remains a challenging and relatively underexplored area of research.

In this workshop, we bring together researchers in dynamical systems,
time-series methods, causality, infinite-depth neural networks, and machine
learning. We believe a side-by-side discussion of dynamical systems and
causal inference (discovery and estimation) will allow one to develop novel
approaches, transfer expertise across communities, and enable us to
overcome current limitations of each individual perspective. Connections to
other scientific disciplines as well as practitioners’ view will be
highlighted to showcase successful applications of causal inference in
dynamical settings.

We welcome any contributions on ongoing research at the interface of
causality and dynamical systems, including but not limited to:

   -

   Causal discovery in time-varying dynamical settings
   -

   Identification and estimation of causal effects in dynamical systems
   -

   Granger causality
   -

   Data-driven discovery of dynamical laws (e.g. symbolic regression)
   -

   Deep learning for time-series modeling and prediction (e.g. neural ODEs)
   -

   Applications of dynamical modeling and causal inference for scientific
   discovery

Important Dates

Paper submission deadline: September 22, 2022
Notification to authors: October 20, 2022
Camera-ready version: TBA
Workshop date: December 3, 2022

Submission instructions

We invite submissions on on-going research that have not yet been published
in a venue with proceedings. While we welcome unfinished work, submissions
in this track should contain original ideas, connections, or results. The
main body of the submission (including most important figures and tables)
must not exceed 9 pages with unlimited additional space for references and
supplementary material. Reviewers will be asked to judge the main body of
the paper and will not be required to read the supplementary material. Papers
should be submitted anonymously to the OpenReview submission portal
<https://openreview.net/group?id=NeurIPS.cc/2022/Workshop/CDS&referrer=%5BHomepage%5D(%2F)>
as a single pdf formatted using the workshop’s latex style file
<https://docs.google.com/folderview?authuser=0&id=1sfn8-2BQfXYQkJjvR9w343XSyl7340w8>.
All papers will undergo double-blind peer review based on which a subset
will be invited for a contributed talk; all other accepted papers will be
invited to be presented at the poster sessions. The workshop will not have
proceedings.

*Invited speakers*
Vanessa Didelez (University of Bremen)
Karl Friston (University College London)
Biwei Huang (UCSD)
Atalanti Mastakouri (Amazon)
Joris Mooij (University of Amsterdam)
Cristopher Salvi (Imperial College London)

Organizers
Sören Becker (Helmholtz AI)
Alexis Bellot (DeepMind)
Cecilia Casolo  (Helmholtz AI)
Niki Kilbertus (TU Munich, Helmholtz AI)
Sara Magliacane (University of Amsterdam, MIT-IBM Watson AI Lab)
Yuyang (Bernie) Wang (AWS)
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