Causal Assessment in Small‐N Policy Studies

Abstract

The identification of cause‐and‐effect relationships plays an indispensable role in policy research, both for applied problem solving and for building theories of policy processes. Historical process tracing has emerged as a promising method for revealing causal mechanisms at a level of precision unattainable through statistical techniques. Yet historical analyses often produce dauntingly complex causal explanations, with numerous factors emerging as necessary but insufficient causes of an outcome. This article describes an approach that renders complex causal narratives more analytically tractable by establishing measurement criteria for ranking the relative importance of component causes. By focusing on subjectively useful measurement attributes, the approach is well suited to the policy sciences' unique combination of explicitly normative aspirations and a commitment to the systematic assessment of causal claims.