This academic paper introduces a novel condition for identifying average treatment effects (ATE) and average treatment effects on the treated (ATT) in observational studies, extending beyond traditional assumptions like unconfoundedness and overlap. The authors propose an "Identifiability Condition" that is both sufficient and necessary for these causal effects to be determined, integrating concepts from statistical learning theory. The research demonstrates how this condition applies to various scenarios, including those where unconfoundedness or overlap are violated, such as in Regression Discontinuity designs and studies with extreme propensity scores. Furthermore, the paper provides finite sample estimation guarantees for these complex scenarios, offering concrete algorithms for practical application and bridging previously disconnected areas of causal inference and learning theory.