This chapter introduces causal graphs as the foundation of qualitative causal inference. The approach begins with a bias-saturated graph that incorporates potential sources of systematic error, including endogeneity, spuriousness, confounding, and measurement error. Through theoretical and empirical testing, researchers assess whether this graph can be simplified into a bias-free representation, enabling more valid unit-level causal inferences. Beyond their methodological role, causal graphs offer clear communicative and heuristic advantages: they reduce ambiguity in visual representations, provide a shared language across methodological traditions, and require systematic reflection on potential causal relationships. The chapter proceeds in four stages. First, it introduces the basic elements of causal graphs and their role in defining paths relevant for causal inference. Second, it outlines three path-specific criteria – front-, back-, and side-door paths – that identify sources of bias and establish conditions for valid inference. Third, it situates qualitative causal inference relative to the detective model and identification-based inference, highlighting the hybrid nature of the proposed approach. Finally, the chapter develops two new concepts: hypothetical interventions and invariance as tools for causal interpretation, and a reconceptualized view of data-generating processes that reconciles probabilistic graph theory with qualitative traditions.