How can regression-type studies, which try to get causal inferences by controlling for variables until whatever is left is plausibly a causal relationship, benefit as much as possible from qualitative research? This chapter offers three qualitative research designs that can help test and improve quantitative causal inferences based on control variables. First, we consider process-tracing designs that seek to discover previously unknown confounding variables that can be added as new control variables, thereby refining and improving the statistical model. Second, we discuss process tracing that seeks to confirm that control variables have a direct connection to the outcome variable, and therefore are relevant controls. Third, we discuss process tracing to check whether control variables might be posttreatment and therefore at least partially harmful. Finally, we provide a demonstration of the kind of iterated interaction that we have in mind between statistical efforts at causal inference and qualitative research to learn more about the assumptions behind the model, involving the relationship between labor union strength and the prevalence of employment law across U.S. states.