George and Bennett on case study methodology

 

Establishing causal relationships within the fabric of the social world is more challenging than in the biological or physical-chemical domains. The reasons for this difficulty are familiar — the high degree of contextuality and contingency that is characteristic of social change, the non-deterministic character of social causation, and the fact that most social outcomes are the result of unforeseen conjunctions of independent influences, to name several.

Alexander George and Andrew Bennett argue for the value of a case-study method of social research in Case Studies and Theory Development in the Social Sciences. The idea here is that social researchers can learn about the causation of particular events and sequences by examining them in detail and in comparison with carefully selected alternative examples.

Here is how they describe the case-study method:

The method and logic of structured, focused comparison is simple and straightforward. The method is “structured” in that the researcher writes general questions that reflect the research objective and that these questions are asked of each case under study to guide and standardize data collection, thereby making systematic comparison and cumulation of the findings of the cases possible. The method is “focused” in that it deals only with certain aspects of the historical cases examined. The requirements for structure and focus apply equally to individual cases since they may later be joined by additional cases. (67)

George and Bennett believe that the techniques and heuristics of the case study approach permit the researcher to arrive at rigorous and differentiated hypotheses about underlying social processes. In particular, they believe that the method of process-tracing has substantial power in social research, permitting the researcher to move from the details of a particular historical case to more general hypotheses about causal mechanisms and processes in other contexts as well (6). They discourage research strategies based on the covering-law model, in which researchers would seek out high-level generalizations about social events and outcomes: “highly general and abstract theories … are too general to make sharp theoretical predictions or to guide policy” (7). But they also note the limits of policy relevance of “independent, stable causal mechanisms” (7), because social mechanisms interact in context-dependent ways that are difficult or impossible to anticipate. It is therefore difficult to design policy interventions based on knowledge of a few relevant and operative mechanisms within the domain of behavior the policy is expected to govern, since the workings of the mechanisms in concrete circumstances are difficult to project.

Fundamentally they align with the causal mechanisms approach to social explanation. Here is how they define a causal mechanism:

We define causal mechanisms as ultimately unobservable physical, social, or psychological processes through which agents with causal capacities operate, but only in specific contexts or conditions, to transfer energy, information, or matter to other entities. In so doing, the causal agent changes the affected entity’s characteristics, capacities, or propensities in ways that press until subsequent causal mechanisms act upon it. (137)

And they believe that the case-study method is a suite of methodological approaches that permit identification and exploration of underlying causal mechanisms.

The case study approach – the detailed examination of an aspect of a historical episode to develop or test historical explanations that may be generalizable to other events – has come in and out of favor over the past five decades as researchers have explored the possibilities of statistical methods … and formal models. (5)

The case study method is designed to identify causal connections within a domain of social phenomena.

Scientific realists who have emphasized that explanation requires not merely correlational data, but also knowledge of intervening causal mechanisms, have not yet had much to say on methods for generating such knowledge. The method of process-tracing is relevant for generating and analyzing data on the causal mechanisms, or processes, events, actions, expectations, and other intervening variables, that link putative causes to observed effects. (214)

How is that to be accomplished? The most important tool that George and Bennett describe is the method of process tracing. "The process-tracing method attempts to identify the intervening causal process--the causal chain and causal mechanism--between an independent variable (or variables) and the outcome of the dependent variable" (206). Process tracing requires the researcher to examine linkages within the details of the case they are studying, and then to assess specific hypotheses about how these links might be causally mediated. 

Suppose we are interested in a period of violent mobilization VM in the countryside at time t, and we observe a marked upswing of religious participation RP in the villages where we have observations. We might hypothesize that the surge of religious participation contributed causally to the political mobilization that ensued. But a process-tracing methodology requires that we we consider as full a range of alternative possibilities as we can: that both religious and political activism were the joint effect of some other social process; that religious participation was caused by political mobilization rather than caused that mobilization; that the two processes were just contingent and unrelated simultaneous developments. What can we discover within the facts of the case that would allow us to disentangle these various causal possibilities? If RP was the cause of VM, there should be traces of the influence that VM exerted within the historical record -- priests who show up in the interrogation cells, organizational linkages that are uncovered through archival documents, and the like. This is the work of process tracing in the particular case. And I agree with George and Bennett that there is often ample empirical evidence available in the historical record to permit this kind of discovery.

Finally, George and Bennett believe that process-tracing can occur at a variety of levels:

The simplest variety of process-tracing takes the form of a detailed narrative or story presented in the form of a chronicle that purports to throw light on how an event came about.... A substantially different variety of process-tracing converts a historical narrative into an analytical causal explanation couched in explicit theoretical forms.... In another variety of process-tracing, the investigator constructs a general explanation rather than a detailed tracing of a causal process. (210-211)

One of the strengths of the book is an appendix presenting a very good collection of research studies that illustrate the case study methodology that they explore. There are examples from American politics, comparative politics, and international relations. These examples are very helpful because they give substance to the methodological ideas presented in the main body of the book.

Geddes on methods

Earlier posts have examined some recent thinking about social science methods (link, link). Here I will examine another recent contributor to this field, Barbara Geddes.

Geddes is a specialist in comparative politics, and her 2003 Paradigms and Sand Castles: Theory Building and Research Design in Comparative Politics is a thoughtful contribution to the debate about how the social sciences should proceed. Her central concern is with the topic of research design in comparative politics. How should a comparative researcher go about attempting to explain the varying outcomes we observe within the experiences of otherwise similar countries? How can we gain empirical grounds for validating or rejecting causal hypotheses in this field? And how do general theories of politics fare as a basis for explaining these concrete trajectories -- the rise of authoritarianism in one country, the collapse of communism in the USSR, an outbreak of democracy in that country, or a surprising populism in another? Geddes finds that the theories that guided comparative politics in the sixties, seventies, and eighties proved to be inadequate to the task of explaining the twists and turns the political systems of the world took during those decades and argues that the discipline needs to do better.

Geddes's proposed solution to this cul de sac is to bring theory and research design closer together. She wants to find a way of pursuing research in comparative politics that permits for more accumulation of knowledge in the field, both on the side of substantial empirical findings and well grounded theoretical premises. Theoretical premises need to be more carefully articulated, and plans for data collection need to be more purposefully guided so the resulting empirical findings are well suited to evaluating and probing the theoretical premises. Here is a good summary paragraph of her view:

The central message of this book is that we could steer a course through that narrow channel between untested theory and atheoretical data more successfully, and thus accumulate theoretical knowledge more rapidly, if certain research norms were changed. Although research norms are changing, basic principles of research design continue to be ignored in many studies. Common problems include inappropriate selection of cases from which to draw evidence for testing theories and a casual attitude towards nonquantitative measurement, both of which undermine the credibility of evidence gathered to support arguments. The failure to organize and store evidence in ways that make it accessible to others raises the cost of replication and that also slows theoretical progress. Uncritical acceptance by readers of theories that have not undergone systematic empirical test exacerbates the problem. (5)

What does Geddes mean by "theory" in this context? Her examples suggest that she thinks of a theory as a collection of somewhat independent causal hypotheses about a certain kind of large social outcome -- the emergence of democracy or the occurrence of sustained economic development, for example. So when she discusses the validity of modernization theory, she claims that some components were extensively tested and have held up (the correlation between democracy and economic development, for example; 9), whereas other components were not adequately tested and have not survived (the claim that the diffusion of values would rapidly transform traditional societies; 9).

Geddes does not explicitly associate her view of social science inquiry with the causal mechanisms approach. But in fact the intellectual process of inquiry that she describes has a great deal in common with that approach. On her view of theory, the theory comes down to a conjunction of causal hypotheses, each of which can in principle be tested in isolation. What she refers to as “models” could as easily be understood as schematic descriptions of common social mechanisms (33). The examples she gives of models are collective action problems and evolutionary selection of social characteristics; and each of these is a mechanism of social causation.

She emphasizes, moreover, that the social causal factors that are at work in the processes of political and economic development generally work in conjunction with each other, with often unpredictable consequences.

Large-scale phenomena such as democratic breakdown, economic development, democratization, economic liberalization, and revolution result from the convergence of a number of different processes, some of which occur independently from others. No simple theory is likely to explain such compound outcomes.  Instead of trying to "explain" such compound outcomes as wholes, I suggest a focus on the various processes that contribute to the final outcome, with the idea of theorizing these processes individually. (27)

What Geddes's conception of "theory" seems to amount to is more easily formulated in the language of causal mechanisms. We want to explain social outcomes at a variety of levels of scale -- micro, meso, macro. We understand that explanation requires discovery of the causal pathways and processes through which the outcome emerged. We recognize that social outcomes have a great deal of contingency and path dependency, so it is unlikely that a great outcome like democratization will be the result of a single pervasive causal factor. Instead, we look for mid-level causal mechanisms that are in place in the circumstances of interest -- say the outbreak of the Bolshevik uprising; and we attempt to discern the multiple causal factors that converged in these historical circumstances to bring about the outcome of interest. The components of theories to which Geddes refers are accounts of reasonably independent causal mechanisms and processes, and they combine in contingent and historically specific ways.

And in fact she sometimes adopts this language of independent mid-level causal mechanisms:

To show exactly what I mean, in the pages that follow I develop a concrete research strategy that begins with the disaggregation of the big question — why democratization occurs — into a series of more researchable questions about mechanisms. The second step is a theorization of the specific process chosen for study — in this case, the internal authoritarian politics that sometimes lead to transition. The third step is the articulation of testable implications derived from the theorization. (43)

And later:

I argued that greater progress could be made toward actually understanding how such outcomes [as democratization and authoritarian rule] by examining the mechanisms and processes that contribute to them, rather than through inductive searches for the correlates of the undifferentiated whole. (87)

(This parallels exactly the view taken by McAdam, Tarrow, and Tilly in Dynamics of Contention, where they argue systematically for a form of analysis of episodes of contention that attempts to identify recurring underlying processes and mechanisms.)

It emerges that what Geddes has in mind for testing mid-level causal hypotheses is largely quantitative: isolate a set of cases in which the outcome is present and examine whether the hypothesized causal factor varies appropriately across the cases. Do military regimes in fact persist with shorter average duration than civilian authoritarian regimes (78)? Like King, Keohane, and Verba in Designing Social Inquiry: Scientific Inference in Qualitative Research, Geddes is skeptical about causal methods based on comparison of a small number of cases; and like KKV, she is critical of Skocpol's use in States and Social Revolutions: A Comparative Analysis of France, Russia and China of Mill's methods in examining the handful of cases of social revolution that she examines. This dismissal of small-N research represents an unwelcome commitment to methodological monism, in my view.

In short, I find Geddes's book to be a useful contribution that aligns more closely than it appears with the causal mechanisms approach to social research. It is possible to paraphrase Geddes's approach to theory and explanation in the language of causal mechanisms, emphasizing meso-level analysis, conjunctural causation, and macro-level contingency. (More on this view of historical causation can be found here.)

Geddes's recommendations about how to probe and test the disaggregated causal hypotheses at which the researcher arrives represent one legitimate approach to the problem of giving greater empirical content to specific hypotheses about causal mechanisms. It is regrettable, however, that Geddes places her flag on the quantitative credo for the social sciences. One of the real advantages of the social mechanisms approach is precisely that we can gain empirical knowledge about concrete social mechanisms through detailed case studies, process tracing, and small-N comparisons of cases that is not visible at the level of higher-level statistical regularities. (A subsequent post will examine George and Bennett, Case Studies and Theory Development in the Social Sciences (Belfer Center Studies in International Security), for an alternative view of how to gain empirical knowledge of social processes and mechanisms.)

Social mechanisms and ABM methods

One particularly appealing aspect of agent-based models is the role they can play in demonstrating the inner workings of a major class of social mechanisms, the group we might refer to as mechanisms of aggregation. An ABM is designed to work out how a field of actors of a certain description, in specified kinds of interaction, lead through time to a certain kind of aggregate effect. This class of mechanisms corresponds to the upward strut of Coleman's boat. This is certainly a causal story; it is a generative answer to the question, how does it work?

However, anyone who thinks carefully about causation will realize that there are causal sequences that occur only once. Consider this scenario: X occurs, conditions Ci take place in a chronological sequence, and Y is the result. So X caused Y through the causal steps instigated by Ci. We wouldn't want to say the complex of interactions and causal links associated with the progress of the system through Ci as a mechanism linking X to Y; rather, this ensemble is the particular (in this case unique) causal pathway from X to Y. But when we think about mechanisms, we generally have in mind the idea of "recurring causal linkages", not simply a true story about how X caused Y in these particular circumstances. In other words, for a causal story to represent a mechanism, it needs to be a story that can be found to hold in an indefinite number of cases. Mechanisms are recurring complexes of causal sequences.

An agent-based model serves to demonstrate how a set of actors give rise to a certain aggregate outcome. This is plainly a species of causal argument. But it is possible to apply ABM methods to circumstances that are unique and singular. This kind of ABM model lacks an important feature generally included in the definition of a mechanism-- the idea of recurrence across a number of cases. So we might single out for special attention those ABMs that identify and analyze processes that recur across multiple social settings. Here we might refer, for example, to the "Schelling mechanism" of residential segregation. There are certainly other unrelated mechanisms associated with urban segregation -- mortgage lending practices or real estate steering practices, for example. But the Schelling mechanism is one contributing factor in a range of empirical and historical cases. And it is a factor that works through the local preferences of individual actors.

So this seems to answer one important question: in what ways can ABM simulations be said to describe social mechanisms? They do so when (i) they describe an aggregative process through which a given meso-level outcome arises, and (ii) the sequence they describe can be said to recur in multiple instances of social process.

A question that naturally arises here is whether there are social mechanisms that fall outside this group. Are there social mechanisms that could not be represented by an ABM model? Or would we want to say that mechanisms are necessarily aggregative, so all mechanisms should be amenable to representation by an ABM?

This is a complicated question. One possible response seems easily refuted: there are mechanisms that work from meso level (organizations) to macro level (rise of fascism) that do not invoke the features of individual actors. Therefore there are mechanisms that do not conform strictly to the requirements of methodological individualism. However, there is nothing in the ABM methodology that requires that the actors should be biological individuals. Certainly it is possible to design an ABM representing the results of competition among firms with different behavioral characteristics. This example still involves an aggregative construction, a generation of the macro behavior on the basis of careful specification of the behavioral characteristics of the units.

Another possible candidate for mechanisms not amenable to ABM analysis might include the use of network analysis to incorporate knowledge-diffusion characteristics into analysis of civil unrest and other kinds of social change. It is sometimes argued that there are structural features of networks that are independent of actor characteristics and choices. But given that ABM theorists often incorporate aspects of network theory into their formal representations of a social process, it is hard to maintain that facts about networks cannot be incorporated into ABM methods.

Another candidate is what Chuck Tilly and pragmatist sociologists (Gross, Abbott, Joas) refer to as the "relational characteristics" of a social situation. Abbott puts the point this way: often a social outcome isn't the result of an ensemble of individuals making discrete choices, but rather is a dance of interaction in which each individual's moves both inform and self-inform later stages of the interaction. This line of thought seems effective as a rebuttal to methodological individualism, or perhaps even analytical sociology, but I don't think it demonstrates a limitation of the applicability of ABM modeling. ABM methods are agnostic about the nature of the actors and their interactions. So it is fully possible for an ABM theorist to attempt to produce a representation of the iterative process just described; or to begin the analysis with an abstraction of the resultant behavioral characteristics found in the group.

I've argued here that it is legitimate to postulate meso-to-meso causal mechanisms. Meso-level things can have causal powers that allow them to play a role in causal stories about social outcomes. I continue to believe that is so. But considerations brought forward here make me think that even in cases where a theorist singles out a meso-meso causal mechanism, he or she is still offering some variety of disaggregative analysis of the item to be explained. It seems that providing a mechanism is always a process of delving below the level of the explananda to uncover the underlying processes and causal powers that bring it about.

So the considerations raised here seem to lead to a strong conclusion -- that all social mechanisms can be represented within the framework of an ABM (stipulating that ABM methods are agnostic about the kinds of agents they postulate). Agent-based models are to social processes as molecular biology is to the workings of the cell.

In fact, we might say that ABM methods simply provide a syntax for constructing social explanations: to explain a phenomenon, identify some of the constituents of the phenomenon, arrive at specifications of the properties of those constituents, and demonstrate how the behavior of the constituents aggregates to the phenomenon in question.

(It needs to be recognized that identifying agent-based social mechanisms isn't the sole use of ABM models, of course. Other uses include prediction of the future behavior of a complex system, "what if" experimentation, and data-informed explanations of complex social outcomes. But these methods certainly constitute a particularly clear and rigorous way of specifying the mechanism that underlies some kinds of social processes.)