Geometry! For over two thousand years it was one of the criteria for recognition as an educated person to be acquainted with the subject of geometry. Euclidean geometry, of course.

In the golden era of Greek civilization around 400 BC, geometry was studied rigorously and put on a firm theoretical basis – for intellectual satisfaction, the intrinsic beauty of many geometrical results, and the utility of the subject. For example, it was written above the door of Plato's Academy ‘Let no-one ignorant of Geometry enter here!’ Indeed, Archimedes is said to have used the reflection properties of a parabola to focus sunlight on the sails of the Roman fleet besieging Syracuse and set them on flame.

For two millennia the children of those families sufficiently well-off to be educated were compelled to have their minds trained in the noble art of rigorous mathematical thinking by the careful study of translations of the work of Euclid. This involved grasping the notions of axioms and postulates, the drawing of suitable construction lines, and the careful deduction of the necessary results from the given facts and the Euclidean axioms – generally in two-dimensional or three-dimensional Euclidean space (which we shall denote by ℝ2 and ℝ3, respectively). Indeed, in the 1700s and 1800s popular publications such as The Lady's and Gentleman's Diary published geometric problems for the consideration of gentlefolk at their leisure.

My approach to the subject of causality has been self-consciously syncretic, drawing from many currents of scholarship. Yet readers may wonder if I have covered this ground in a truly comprehensive fashion. Indeed, several topics of current interest are treated schematically, or not at all, in the foregoing chapters.

In this chapter, which functions as a coda to the third part of the book, I briefly review approaches to causal inference that seem, at least on the face of things, anomalous. This includes causal-process observations, causes-of-effects, necessary/sufficient arguments, and qualitative comparative analysis (QCA). As many of these topics overlap, each of the following sections builds on the previous.

The Little Prince articulates the invidious, de-humanizing element inherent in any attempt to measure, and thereby compare, human beings. “Treating them like statistics,” as the phrase goes. Abhorrent though it may seem (and surely, the measurement of intimate material and emotional states is an act of extreme hubris), there may also be good reasons for measuring, say, the incomes of families in a community.

Having laid out a framework of tasks, strategies, and criteria that define social science methodology, I now want to discuss how this framework maps onto the “paradigm wars” that have roiled the disciplines of social science over the past half-century.

For outsiders, as well as for many insiders, the distinctions evident across disciplines, methods, and schools are paramount. We sit at separate tables. Yet there are also many things that we share. Moreover, there is little profit in emphasizing our differences, given that the presumed objective of scientific deliberation is, ultimately, to reach consensus. Incommensurability is not conducive to a productive interchange of ideas. If taken seriously, it prohibits knowledge cumulation. Consequently, this book has emphasized the methodological coherence of the social sciences.

Preface

The field of social science methodology has been hyperactive over the past several decades. Methods, models, and paradigms have multiplied and transformed with dizzying speed, fostering a burst of interest in a heretofore moribund topic. One sign of the growing status of this field is the scholarly vituperation it inspires. Terms such as interpretivism, rational choice, poststructuralism, constructivism, randomization, positivism, and naturalism are not just labels for what we do; they are also fighting words.

Meanwhile, venerable debates over power, class, and status seem to have subsided. It is not that we no longer talk about these subjects, or care about them. Yet there appears to be greater consensus within the academy on normative political issues than there was, say, in the 1960s and 1970s. We are all social democrats now – for better, or for worse. Debates continue, especially over the role of race, gender, and identity. However, they do not seem to be accompanied by a great deal of rancor. Thus, over the past few decades methodological disagreements have largely displaced disagreements over substantive issues as points of conflict at conferences, at faculty meetings, and on editorial boards. Methodology, not ideology, seems to define the most important cleavages within the social sciences today.

The subject of this book is the set of disciplines known as the social sciences (which in earlier times would have been referred to as the moral or human sciences). By this is meant a scientific study of human action focusing on elements of thought and behavior that are in some degree social (nonbiological). “The object of the social sciences,” writes Hans Morgenthau, “is man, not as a product of nature but as both the creature and the creator of history in and through which his individuality and freedom of choice manifest themselves.” Wherever nurture matters more than nature, or where some significant decisional element is involved, we are on the turf of social science. (This does not mean that genetic dispositions are eliminated from consideration; indeed, they comprise an active research agenda in the social sciences today. However, one presumes that any outcome of interest to the social sciences is not entirely biologically determined; there must be a significant component of choice.)

Having discussed the formal (super-empirical) criteria of a good argument, we turn now to the empirical portion of social science research, the hoped-for encounter with reality. This stage may be referred to variously as analysis, assessment, corroboration, demonstration, empirics, evaluation, methods, proof, or testing. (While acknowledging the subtle differences among these terms, I shall treat them as part of the same overall enterprise.)

Of course, the distinction between theory formation and theory-testing is never clear and bright. As is the case everywhere in social science, tasks intermingle. One cannot form an argument without considering the empirical problem of how to appraise it, and vice versa. Moreover, the task of (dis)-confirming theories is intimately conjoined with the task of forming theories. As Paul Samuelson notes, “It takes a theory to kill a theory.”

I hope that my chosen approach to social science methodology strikes readers as commonsensical. Indeed, none of the tasks, strategies, and criteria were invented by the author (though I have chosen labels for things that do not have established names), and most have received extensive discussion. From this perspective, the present book qualifies as a compendium of truisms – a function shared, I might add, by any integrative work on methodology. The first, and perhaps most important, justification for the proposed framework is that it represents a formalization of what we already know.

Nevertheless, readers are bound to have qualms about some elements of the argument. They might take issue with the criterion of generality, for example. They might like what I have to say about description, but not about causation. On what grounds might one adjudicate this sort of dispute?

Chapter 9 set forth general criteria applying to research designs whose purpose is to assess causal relationships. In this chapter and Chapter 11 I lay out specific strategies of causal inference. To be sure, there is a high degree of overlap between these topics. Indeed, each strategy can be analyzed according to the criteria that it fulfills (or does not fulfill). Yet the shift from criteria to strategies is an important one. While Chapter 9 is about principles, Chapters 10 and 11 come closer to a “how-to” guide, complete with detailed discussions of specific studies.

Strategies of causal inference will be divided into three categories: (1) randomized designs; (2) nonrandomized designs; and (3) strategies that move beyond X and Y, as summarized in Table 10.1. Because this covers a lot of ground the material is divided into two chapters, with this chapter focused on the first two topics and Chapter 11 on the third.