STCD: A Spatio-Temporal Causal Discovery Framework for Hydrological Systems

Causal learning has become an essential attribute in majority of the machine learning models. One of the widely studied fields in causal learning is causal discovery which aims to identify potential cause-effect relationships from observational data. Temporal causal discovery models are specifically curated to enforece the temporal constraints while discovering the causal relationships. However, in physical systems such as hydrological systems, there are additional constraints such as spatial constraints that play a crucial role in deciding whether a node is a causal parent for another node or not. Failing to enforce these additional constraints may mislead the model to classify an irrelevant relationship as a causal relationship. Furthermore, causal discovery models are evaluated against a ground truth causal graph. However, the hydrological systems contain a huge number of features making it challenging to obtain a ground-truth causal graph. To deal with the aforementioned problems, in this study we propose a new Spatio-Temporal Causal Discovery Framework named, STCD. By enforcing temporal and spatial constraints STCD aims at identifying meaningful causal relationships. Furthermore, to evaluate the causal relations inferred by STCD in the absence of the ground-truth causal graph, we utilize only the causal parents of a target variable for prediction across different years. We demonstrate that utilizing only the causal features identified by STCD to predict the flow-rate for a target location attains superior performance.