Connectionists: Announcement UAI 2023 Workshop "Workshop for Causal Inference for Time Series Data", Submission Deadline: May 31, 2023

[Wahl, Jonas]

Call for papers

Workshop for Causal Inference for Time Series Data at UAI 2023, Pittsburgh, USA:

https://sites.google.com/view/ci4ts2023/home



Important Dates

Paper submission deadline: May 31, 2023 23:59 AoE
Notification to authors: July 4, 2023 23:59 AoE
Workshop date: August 4, 2023


Topic and motivation



Many important research questions involve causation in systems for which direct experimentation is expensive, unethical or quite simply impossible. Examples include the Earth system, the human brain, socio-economic systems, epidemiology and industrial processes. Research on causal inference aims to provide both theoretical foundations and practical methods that can use domain knowledge and observational or experimental data to learn and quantify possible causal relationships between the variables of interest. Most real world data comes in the form of time series, which pose special difficulties for causal inference, and have been the subject of statistical study since the beginning of the 20th century. To deal with the challenges posed by time series data, several theoretical frameworks and practical methods have been developed, each based on their own set of underlying assumptions. Some of these approaches rely on the theory of continuous-time dynamical systems, others make use of results on stochastic processes or adapt the constraint-based approach to causality to time series data. Although recent works have made advances on several fronts, many challenges remain and causal inference for time series is arguably still a relatively underexplored area of research.


In this workshop, we aim to bring together leading researchers and new investigators on causal inference for time series, as well as experts in dynamical systems and stochastic processes.


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


  *   Causal structure learning on time series data

  *   Causal effect estimation, from adjustment to do-calculus, on time series

  *   Counterfactual reasoning on time series

  *   Interventions for time-dependent causal models

  *   Time series root cause analysis

  *   Causal representation learning for time series

  *   Causal modeling of time-scale or frequency-dependent relations

  *   Frequency-space causal inference and structure learning

  *   Dynamical systems-based causal inference

  *   Stochastic process-based causal inference

  *   Benchmarks simulating real-world challenges

  *   Example applications from different scientific domains (Earth sciences, neuroscience, economy, etc)



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, new connections between research fields, or novel results. The main body of the submission,including figures and tables, must not exceed 8 pages plus one additional page for references. There is no page restriction for 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=auai.org/UAI/2023/CI4TS> as a single pdf formatted using the workshop’s latex style file<https://www.auai.org/uai2023/submission_instructions>. All papers will undergo double-blind peer review. A subset of the accepted papers 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.

Organizers

  *   Jakob Runge1,2, jakob.runge at dlr.de<mailto:jakob.runge at dlr.de>, https://climateinformaticslab.com/about/

  *   Urmi Ninad2, urmi.ninad at tu-berlin.de<mailto:urmi.ninad at tu-berlin.de>, https://climateinformaticslab.com/about/

  *   Jonas Wahl2, wahl at tu-berlin.de<mailto:wahl at tu-berlin.de>, https://jonaswahl.com/

  *   Andreas Gerhardus1, andreas.gerhardus at dlr.de<mailto:andreas.gerhardus at dlr.de>, https://climateinformaticslab.com/about/

  *   Sara Magliacane3,4, s.magliacane at uva.nl<mailto:s.magliacane at uva.nl>, https://saramagliacane.github.io/

  *   Charles Assaad5, cassaad at easyvista.com<mailto:cassaad at easyvista.com>, https://ckassaad.github.io/

  *   Tom Claassen6, tom.claassen at ru.nl<mailto:tom.claassen at ru.nl>, http://www.cs.ru.nl/~tomc/

  *   Clark Glymour7, cg09 at andrew.cmu.edu<mailto:cg09 at andrew.cmu.edu>, https://www.cmu.edu/dietrich/philosophy/people/emeritus/glymour.html






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