Call for Papers

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

• Time-scales and 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 underlying (nonlinear) dynamical systems and their equilibria

• Benchmarks simulating real-world challenges

• Applications from different scientific domains (Earth sciences, Epidemiology, Neuroscience, Economy, etc)

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 as a single pdf to the OpenReview submission portal;

• Papers should be 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.

• Submission Start: April 5

• Submission Deadline: May 19 2024 11:59AM (AoE)

• Author notification: June 14 2024 11:59AM (AoE).