This chapter introduces event-history maps, the third conceptual innovation of qualitative causal inference alongside hypothetical interventions and invariant causal mechanisms. Event-history maps link the theoretical domain of causal graphs to the empirical domain of observed events, case studies, and hypothesis testing. They represent the realized values of the random variables in a causal graph, taking the form of a sequence of events structurally isomorphic to the graph. While causal graphs depict variables and potential relationships, event-history maps depict specific outcomes and temporal sequences. Only when paired with a bias-free causal graph can such maps credibly represent chains of causal relationships. The chapter illustrates how event-history maps can be used to strengthen within-case analysis. It reconstructs implied graphs and maps from published studies to show how existing research often reverts to the detective model, missing opportunities to test additional empirical implications. By explicitly pairing causal graphs with event-history maps, researchers can probe causal claims more effectively and avoid invalid inferences. The chapter concludes with a detailed example of evaluating the three path-specific criteria of valid unit-level inference, showing how event-history maps serve as essential tools for empirically validating causal graphs and improving the rigor of qualitative causal analysis.