Large networked systems -- ranging from communication networks and power grids to social and transportation networks -- exhibit intricate interactions and dynamic dependencies among the agents that constitute them. In many domains, decision-makers need to understand how interventions such as policy changes, infrastructural modifications, or emergency responses will impact the system before implementing the interventions. Conducting real-world interventions in complex systems can be prohibitively risky with inadvertent consequences. In such circumstances, hypothetical evaluations allow decision-makers to simulate interventions and assess their potential impacts without exposing the system to real-world risks. This is particularly important when interventions have irreversible or costly consequences, as it enables planning and preparation for adverse outcomes. The overarching goal of this project is to design a theoretically principled framework for performing accurate hypothetical interventions on complex systems and predicting their outcomes, thereby providing a reliable basis for planning and risk assessment. This is especially crucial in environments where erroneous predictions or suboptimal decisions could lead to significant performance, robustness, or safety consequences. The project consists of several educational components aimed at students at different levels (high school, undergraduate, and graduate) as well as contributions to the educational missions of the releva