This chapter shifts the focus from causal inference to causal explanation, emphasizing that valid explanations require more than the identification of causal effects. While causal inference addresses “what if” questions by tracing the consequences of interventions, causal explanation responds to “why” questions by reconstructing the sequence of causes that produced an outcome. The chapter proposes a framework of explanatory adequacy, defined in terms of depth, density, and relevance. It argues that qualitative causal inference, by rigorously addressing sources of bias and systematically evaluating causal paths, generates richer and more meaningful explanations of singular outcomes than inference alone can provide. The chapter situates this discussion within broader debates about the “credibility revolution” in causal inference, noting the growing recognition that credible identification does not automatically yield satisfactory explanations.