Successfully tackling many urgent challenges in socio-economically critical domains, such
as public health and sustainability, requires a deeper understanding of causal relationships …

The capacity to anticipate streamflow is critical to the efficient functioning of reservoir
systems as it gives vital information to reservoir operators about water release quantities as …

This survey paper covers the breadth and depth of time-series and spatiotemporal causality
methods, and their applications in Earth Science. More specifically, the paper presents an …

Streamflow plays an essential role in the sustainable planning and management of national
water resources. Traditional hydrologic modeling approaches simulate streamflow by …

Many important phenomena in scientific fields such as climate, neuroscience, and
epidemiology are naturally represented as spatiotemporal gridded data with complex …

While witnessing the exceptional success of machine learning (ML) technologies in many
applications, users are starting to notice a critical shortcoming of ML: correlation is a poor …

Disaster response agencies have been shifting from a paradigm of climate forecasting
towards one of anticipatory action: assessing not just what the climate will be, but how it will …

Causal structure learning from observational data has been an active field of research over
the past decades. In the literature, different algorithms and models have been proposed …