Recent advances in data science and statistics have revolutionized how researchers uncover cause-and-effect relationships from complex, real-world data. Many pressing questions—such as whether flu vaccination reduces infection rates, whether sanitation programs improve children’s health, or whether educational policies enhance student outcomes—cannot be answered through randomized experiments alone. Observational data, while abundant, often pose serious challenges due to hidden biases, unmeasured factors, or interconnected influences among individuals. For example, a person’s risk of flu depends not only on their own vaccination status but also on whether people around them are vaccinated, while unmeasured behaviors such as health-seeking habits can distort results. This project tackles these challenges by developing advanced statistical methodologies that improve the reliability of causal conclusions. In particular, it enhances a class of techniques known as distributional balancing methods, which create fair, comparable groups across the full range of observed variables. By extending these methods to account for complex data structures and unobserved confounding, the project will equip scientists and policymakers with more trustworthy evidence for decision-making. The research outcomes will impact healthcare, education, economics, and environmental policy, while also contributing to science through open-source software, user-friendly resources, and the training of students