A developmental approach to historical causal inference

Empirical historical research typically falls into one of three categories: the study of major historical events; the use of "history as data" to test general theories; and the study of the legacies of historical processes. We argue that because of data sparsity and dynamically unfolding processes, the study of major historical events is less well suited to design-based inference than other types of historical research. Drawing examples from our own work, we propose a set of researchproceduresfor designing causally oriented work, and argue that the construction of a "timeline of relevant counterfactual nodes" can facilitate the organization of a research project investigating complex historical processes. The researcher can focus on relevant counterfactual moments as potential episodes of change using either statistical or qualitative techniques as appropriate, moving forward through the timeline and updating their beliefs about a hypothesized cause's importance across the process.