Call for Papers: Workshop for Causal Inference for Time Series Data at UAI 2024
Jonas Wahl
Workshop for Causal Inference for Time Series Data at UAI 2024, July 19th 2024, Barcelona, Spain:
https://sites.google.com/view/ci4ts2024/
Important Dates
Paper submission deadline: May 19, 2024 11:59AM AoE
Notification to authors: June 14, 2024 11:59AM AoE
Workshop date: July 19, 2024
Topic and motivation
Many important research questions, from diverse domains such as the Earth system, engineering, the human brain, socio-economic systems, epidemiological studies and industrial processes, involve cause-and-effect relationships in time evolving systems. Research on causal inference aspires to develop both theoretical foundations and practical methods that can combine domain knowledge and observational or experimental data to learn and quantify possible causal explanations of the data. Time-series data brings special opportunities as well as unique challenges for causal inference, and has been the subject of statistical study since the beginning of the 20th century. Recent work on causal inference has made advances on several fronts, including multiple data sets, non-stationarity, statistical tests, identifiability results, optimal causal effect estimation, and other topics.
This workshop aims to bring together leading researchers and new investigators on causal inference for time series as well as experts in dynamical systems and stochastic processes. It is a continuation of the topic and theme of last year’s well-attended UAI workshop on “Causal Inference for Time Series Data” (see https://www.auai.org/uai2023/workshops and https://sites.google.com/view/ci4ts2023/).
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
Time series root cause analysis
Causal representation learning for time series
Causality for time series forecasting and sequential decision-making
Causal modeling of time-scale or frequency-dependent relations
Cycles, non-stationarity, sub-sampling, time aggregation, dating uncertainties
Dealing with multiple (short) time series datasets
Interventions for time-dependent causal models
Relations to dynamical systems and their equilibria
Benchmarks simulating real-world challenges
Applications from different scientific domains (Earth sciences, Epidemiology, 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;
Papers should be formatted using the as a single pdf formatted using the workshop's style file.
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
Charles Assaad1, Andreas Gerhardus2, Wiebke Günther2, 3, Rebecca Herman2, Biwei Huang4, Urmi Ninad3, 2, Oana Popescu2, Jakob Runge2, 3, Nils Sturma5, Jonas Wahl3, 2.
1INSERM, Sorbonne Université, Institut Pierre Louis d’Épidémiologie et de Santé Publique, Paris, France
2German Aerospace Center (DLR), Institute of Data Science, Jena, Germany
3Technische Universität Berlin, Berlin, Germany
4University of California, San Diego, US
5Technische Universität München, München, Germany
Best regards,