Comparative Historical Analysis (CHA) has a strong tradition of analysing long patterns of policy change. The method is useful to theory-testing and theory-building because it looks for all possible causal variables influencing policy change over long periods of time. CHA combines techniques like process tracing, archival research, statistical methods and in-depth interviews to assess causal process observations. From the methodological perspective, time frames of analysis and types of policy change became the centre of discussions about advancing CHA methods in recent years. The result was refinements in the definition of events, critical junctures and sequences. But how to assess policy change and attribute causality when the timeline of observations is long, and changes become embedded or blurred with structural context or the institutional framework? This chapter advances the discussion by proposing the use of an index of policy interventions as a suitable tool to organize, classify and validate the significant amount of causal process observations and quantitative data points, resulting from small-N comparisons. The author argues that time frame validation can be a key component of comparative policy analyses that entail long-term cycles of policy, because it can help identify the type of change at stake and to understand the existence of cumulative causes and/cumulative outcomes that are blurred within a complex structure of events. Furthermore, an index of policy intervention could help to increase internal validity for the time frame of analysis and the importance of sequence in the developing of positive or negative feedback in the process of change.