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.

This chapter introduces qualitative causal inference and explanation, a hybrid method that integrates qualitative case-study analysis with elements of quantitative causal inference. Traditional process tracing emphasizes explanation but risks false positives, while identification-based inference secures validity but offers little guidance for explaining particular outcomes. The chapter argues that adapting core ideas from causal inference – such as identification and bias – into a qualitative idiom can strengthen within-case analysis. It defines qualitative causal inference as a strategy for distinguishing true causal influences from spurious associations and sets out criteria for valid unit-level inference. By combining explanation with valid inference, this approach aims to produce more credible and more satisfying accounts of complex outcomes such as democratic transitions, revolutions, and wars.

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.