This chapter addresses the evaluation of qualitative causal inferences, focusing on how to assess whether claims about causal continuity and discontinuity are credible. Building on the framework of bias-free causal graphs, it explains the iterative process of testing causal relationships through hypothetical interventions. Three types of claims are central: that a causal process could have produced the outcome (front-door continuity), could not have done so via a pre-treatment mechanism (back-door discontinuity), and would not have occurred through other unrelated causes (side-door discontinuity). The chapter develops a multilevel evaluation strategy. At the micro-level, it introduces three measures – theoretical plausibility, mechanistic credibility, and descriptive validity – for judging individual claims about hypothetical interventions between adjacent nodes. At the path level, it examines how these claims aggregate across front-, back-, and side-door paths to assess whether bias can be excluded. At the graph level, it considers the overall plausibility of substituting a bias-free graph for a bias-saturated one, outlining possible combinations of satisfied and unsatisfied criteria. Finally, the chapter proposes remedial measures for cases where bias cannot be eliminated. These include signing the bias, estimating relative magnitudes of effects, and discretizing outcomes. Even when systematic error remains, the method enhances transparency and strengthens the epistemic value of qualitative research.