Due to the critical role causality plays in decision-making, the state of-the-art in machine learning for causality is rapidly evolving. With rapid development and deployment of new models, datasets, and metrics, it is increasingly difficult for researchers and practitioners to identify the most suitable approach for their problem. Models exhibit different performances when they train on different data or even when they are used under different hardware/software platforms, making it challenging for users to select the appropriate setup pertinent to their problem. To address these difficulties, we present a computing framework, CausalBench-ER that serves, not only as a benchmarking platform for causal machine learning models, but also as a resource that can explain benchmarking results across different metrics, software, and hardware setups. Furthermore, CausalBench-ER recommends additional scenarios to consider to help pave the way towards more robust benchmarking.

Recent advances in causal machine learning introduced a plethora of new causal discovery and causal inference models to tackle decision support problems. Yet, these models exhibit different performance when they train on different data, and even different hardware/software platforms, making it challenging for users to select the appropriate setup pertinent to their specific problem instance. The situation is complicated by the fact that, until recently, the field lacked a unified, publicly available, and configurable platform that supports all major causal inference tasks, including causal discovery, causal effect estimation, and causal inference. CausalBench is a comprehensive benchmarking tool for causal machine learning that facilitates accurate and reproducible benchmarking of causal models across metrics and deployment contexts and helps users to select the most appropriate set up (such as hyper-parameter configuration) for the specific problem setting. This tutorial is intended to familiarize attendees from diverse backgrounds, who are interested in causal learning models and with the capabilities of CausalBench. The tutorial begins with an introduction to ''causality'' and causal machine learning, and then provides hands-on experience with CausalBench to equip attendees with the knowledge necessary to utilize CausalBench for their causal learning problems.

Streamflow plays an essential role in the sustainable planning and management of national water resources. Traditional hydrologic modeling approaches simulate streamflow by establishing connections across multiple physical processes, such as rainfall and runoff. These data, inherently connected both spatially and temporally, possess intrinsic causal relations that can be leveraged for robust and accurate forecasting. Recently, spatio-temporal graph neural networks (STGNNs) have been adopted, excelling in various domains, such as urban traffic management, weather forecasting, and pandemic control, and they also promise advances in streamflow management. However, learning causal relationships directly from vast observational data is theoretically and computationally challenging. In this study, we employ a river flow graph as prior knowledge to facilitate the learning of the causal structure and then use the learned causal graph to predict streamflow at targeted sites. The proposed model, Causal Streamflow Forecasting (CSF) is tested in a real-world study in the Brazos River basin in Texas. Our results demonstrate that our method outperforms regular spatio-temporal graph neural networks and achieves higher computational efficiency compared to traditional simulation methods. By effectively integrating river flow graphs with STGNNs, this research offers a novel approach to streamflow prediction, showcasing the potential of combining advanced neural network techniques with domain-specific knowledge for enhanced performance in hydrologic modeling.