People are often interested in making a causal inference from time-series or longitudinal data - data in which one or many individuals are assessed at more than one time point (such as panel data, intensive longitudinal data, multi-trial experimental data and neuroimaging data). Over the past decades, there has been considerable progress in the methods to infer causality from longitudinal data, but they are developed in respective fields in relative isolation, and with limited cross-pollination of ideas across disciplines. In this workshop, experts in various fields (e.g., neuroscience, political science, psychology, epidemiology, philosophy) present methods and ideas on longitudinal data analysis and causal inference. Through the presentations and panel discussions, the workshop aims to interactively highlight the strengths and limitations of causal inference methods on longitudinal data from a highly cross-disciplinary perspective. The workshop is targeted for applied researchers in any fields working on any type of longitudinal or time-series data with the motivation to learn the topic from an interdisciplinary perspective.