This chapter shifts from theory to practice in qualitative causal inference. It addresses the central challenge of how to construct causal graphs that can support valid unit-level causal inferences. Building on the framework of bias-free graphs, hypothetical interventions, invariant causal mechanisms, and event-history maps, the chapter explains how to design causal graphs with credible front-door, back-door, and side-door paths. The first section examines the origins of causal graphs, arguing that while qualitative traditions emphasize alternative hypotheses, they rarely yield structured causal paths. To overcome this gap, the chapter introduces the method of iterated modularity: constructing front-door paths incrementally through sequential pair-wise analyses of adjoining nodes. This approach allows complex causal processes to be decomposed into manageable segments while maintaining methodological rigor. The discussion also engages with objections that causal relations in the social and political world are too complex to yield neat causal paths, countering that temporal sequencing and careful distinction between variables and events can often impose useful structure. The chapter concludes by discussing the role of critical junctures and event-history maps, clarifying how they contribute to evaluating the empirical implications of causal graphs without succumbing to common inferential pitfalls.