Significant methodological and algorithmic advancements in causal inference have recently found applications in several fields, including but not limited to epidemiology and neuroscience. Despite the growing availability of in-situ and remotely sensed hydrological data, the study of causal inference in complex hydrological systems is still in its infancy. In this session, we welcome presentations that: 1) inter-compare the performance of different causal inference methods using simulations or observations within the context of hydrologic systems, 2) formulate and evaluate hypotheses on the underlying structure and dynamics of hydrologic processes, or 3) utilize causal inference frameworks to make attribution statements regarding the impact of anthropogenic global warming on observed changes of hydrologic extremes (e.g., droughts and floods). We also encourage the submission of studies that seek to integrate deep learning within classical frameworks of causal inference (e.g., Granger causality).