Causal inference for time series data

Many important research questions involve causation in systems that evolve in time such as the Earth system, the human brain, socio-economic systems, epidemiological studies and industrial processes. Research on causal inference aspires to provide both theoretical foundations and practical methods that can use 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/home).

• Submission Start: April 5

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

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

• Date of workshop: July 19 2024

ci4ts.uai.2024@gmail.com