This chapter discusses Contribution Analysis (CA), an evaluation method that demonstrates how programs contribute to changes rather than isolating causal effects. Originating from theory-based evaluation, CA emphasizes multiple causality, refining theories of change, and building plausible causal narratives supported by evidence. It evolved to handle complexity, refine theories, and make “good enough” inferences for decision-making, integrating with other approaches for practical use. Two case studies, from Mexico and Nuevo León, show CA's application in educational and social programs, illustrating how it guides decisions, enhances learning, and provides credible evidence for policymakers and communities. CA is especially useful in education and social policy contexts where complexity, limited data, and ethical considerations hinder experimental methods, combining scientific rigor with political relevance to enhance impact evaluation.