Macro-causal analysis pivots between exploring patterns of change and exploring causal patterns. It employs a bounded conception of history, keeping causal factors constant, but expands the length of causal chains to explore how their temporal order affects causal outcomes. It analyzes this causal order in terms of elements of physical time: timing, sequencing, tempo, and duration. In paying attention to these elements, it in effect unfreezes physical time, which defines the existing linear notions of causality. Macro-causal analysis relaxes these linearity assumptions by expanding the analysis from what Pierson called short–short to short–long, long–short, and long–long explanations. It thus recognizes that theories rest on temporal assumptions and that understanding those assumptions invites exploring backgrounded causal factors that can help update theories. This udpating process is abduction, which ultimately makes hypotheses more testworthy. Besides elongating causal chains, this type of analysis also elongates outcomes by paying attention to a range of near-miss outcomse that are frequently overlooked but provide often important new inductive insights.

We live in a constantly changing world, which requires reformulating our research questions. CHA is particularly well suited for describing and redescribing this changing and ultimately historical world. It emerged independently in many different disciplines, and thus is spoken many different vernaculars. This book extracts from those vernaculars a common grammar of time, to place CHA on more systematic methodological foundations. The introduction previews key elements of CHA: its reliance on historical thinking to explore social reality; its deployment of a rich temporal vocabulary; and three distinct strands of CHA, each making its own distinct contributions.

Social inquiry needs representations of social reality, and such representations involve ontological choices of how much to freeze history and geography. The freezing metaphor relates to how much social inquiry relaxes conditional independence and unit homogeneity, the two key assumptions of VBA. Unfreezing history and geography brings to light historical and geographic particularities, makes possible new inductive insights into updating theories, and generates new research questions. Comparisons help turn such explorations into discoveries by identifying patterns. CHA employs four comparisons: cross-sectional, serial, contextual, and historical. It thus differs from VBA, which uses comparison for the strictly methodological purpose of controlling for confounders. CHA goes back to the late nineteenth century and has explored four key themes: the evolution of capitalism, regime changes, the administrative transformation of states, and the changing nature of war. CHA does not have a monopoly on historical thinking. VBA employs historical thinking when exploring confounders and historical boundary conditions that might bias their test results.

Physical time refers to five context-independent elements of time: tempo, duration, timing, sequencing, and stages. It makes two contributions. It refines the analysis of historical time by focusing on the rhytms in which it unfolds. It thus supplements the analysis of what changes in content by paying attention to how it changes in rhythm. It also refines causal analysis by treating the elements of physical time that produce distinct causal effects; these are frequently overlooked by linear notions of causality. CHA employs both physical time and historical time, and this distinguishes it from other methodologies. It configures these elements of time in distinct ways, which define the three strands of CHA: eventful, longue durée, and macro-causal analysis.

This chapter offers a primer of the literature on the origins of proportional representation. Subsequent chapters use this literature to illustrate the elements of CHA. It therefore provides the background information to make those illustration easier tofollow.

Longue durée analysis employs descriptive statistics and data visualization of explore univariate, bivariate, and multivariate trends. These trends tracks historical historical changes in a thinner manner than eventful analysis, but in return they explore such trends over longer time periods and larger numbers of cases. It introduces four graph types: serial scatterplots, contextualized line graphs, developmental typologies, and serial bar graphs that are used to visualize trends. Longue durée analysis frequently redescribes mi-described trends by paying close attention to three sources of misdescription: inadequate baselining, unfreezing of time scales, and mismeasurements. It discusses how using stock instead of flow indicators increases the ability of time series to capture historical changes.

Various CHA scholars have contributed to causal process tracing and helped establish it as principal alternative to VBA. A recent, Bayesian-informed version is particularly relevant to CHA because it shares the same historiographical sensibilities of seeing knowledge evolve through a close dialogue between new findings and the existing foreknowledge. It makes causal inferences conditional on a pre-testing articulation of testworthy hypotheses and juxtaposes new test results onto older ones at the post-testing stage. Process tracing defines the testworthiness of hypotheses according to the number and diversity of empirical implications a theory has before the testing starts. It also defines test strength in terms of the specificity of the hypotheses that are tested and of their uniqueness vis-à-vis the alternative explanations against whichthey are paired. The chapter introduces a new tool, the theory ledger, to help evaluate test strength and to update confidence in causal inferencing.

Historical time is a plural entity, not a singular one. Methodologicals rest on difficult ontological assumptions, which translate into four potential notions of history: cyclical, bounded, serial, and eventful. Cyclical history freezes history the most, by assuming that the present merely repeats the past, and thus makes history de facto reversible. It in effect freezes history, and CHA regards this notion of history as ahistorical. Bounded history freezes certain time intervals during which it assumes that history stands still and the past in effect repeats itself, at least for a limited period of time. It is not interested in how the bounded period is qualitatively different from the periods preceeding or following it. Serial history partially unfreezes history and uses time series data to track secular trends through time. Eventful history is the most unfrozen treatment of the past and tries to identify continuities and discontinuities, or distinct periods. Historical tourism refers to notions of history so static and so frozen that they cease to be historical in any meaningful sense of the word. It identifies variants of historical tourism in history proper and in CHA.

Eventful analysis employs the most unfrozen and hence the most exploratory strand of CHA. It employs historical comparisons and explores transformation patterns, that is, patterns of qualitative change. It uses two key tools: historical description and conceptualization. The aim of historical description is to figure out what is going on, to gain a basic understand of a phenomenon before proceeding to explain it. Often this involves de-redescribing a phenomena that has qualitatively changed over time. Historical description, in turn, involves six concrete steps: fact gathering, chronicling, concatenation, periodizing, looking for intercurrence patterns, and rethinking research questions. Conceptualization serves to make historical description more comparativist and to explore broader patterns. The chapter discusses how to replace proper names with broader concepts by defining both the positive and the negative pole of concepts. It lists criteria for assessing the content and temporcal validity of concepts.

Explaining historical change is difficult because it involves analyzing a moving object. Historical explanations address this problem by dividing historical change into moments of discontinuity and periods of continuity. They explain discontinuities by retracing the generative processes that ultimately produced a change. Historical explanations explain continuities by drawing on path-dependent explanations. Such explanations involve specifying an early mover advantage during a historical discontinuity and following up by identifying so-called increasing return mechanisms that compound the causal effects of the early mover advantage over time. This compounding effect serves to epxlain why certain changes, once they are in place, reproduce themselves and hence endure.