The laboratory develops statistical and machine-learning methods for settings in which the available data do not adequately represent the conditions encountered at deployment. Our research focuses on distribution generalization, causal inference for extreme events, and detecting when models are asked to extrapolate beyond the support of their training data. Drawing on causal inference, extreme value theory, and modern machine learning, we study the assumptions that allow reliable learning under changing conditions and develop diagnostics for when those assumptions fail.