The opening chapter provides a brief outline of the conventional division of labor between qualitative and quantitative methods in the social sciences. It sketches the main standards that govern case study research. It then offers an overview of subsequent chapters, which challenge some of these distinctions or deepen our understanding of what makes qualitative case studies useful for both causal inference and policy practice.

Sarah Glavery and her coauthors draw a distinction between explicit knowledge, which is easily identified and shared through databases and reports, and tacit knowledge, the less easily shared “know how” that comes with having carried out a task. The chapter explores ways to use case study preparation, as well as a case itself, as a vehicle for sharing “know how,” specifically with respect to program implementation. It considers the experiences of four different types of organizations that have used case studies as part of their decision-making as it pertains to development issues: a multilateral agency (the World Bank), a major bilateral agency (Germany’s GIZ), a leading think tank (Brookings), and a ministry of a large country (China’s Ministry of Finance), which are all linked through their involvement in the Global Delivery Initiative.

RCTs have gained considerable prominence as a ‘gold standard’ for establishing whether a given policy intervention has a causal effect, but what do these experiments actually tell us and how useful is this information for policy-makers? Cartwright draws attention to two problems. First, an RCT only establishes a claim about average effects for the population enrolled in an experiment; it tells us little about what lies behind the average. The policy intervention studied might have changed nothing in some instances, while in others it triggered large shifts in behavior or health or whatever is under study. But, second, an RCT also tells us nothing about when we might expect to see the same effect size in a different population. In short, “identifying a cause is not the same as identifying something that is generally true,” Cartwright says. To assess how a different population might respond requires other information of the sort that qualitative case studies often uncover. Cartwright identifies the key elements we need to know in order to decide whether the effects observed in an experiment will scale.

Gonzalez and Widner reflect on the intellectual history of a science of delivery and adaptive management, two interlinked approaches to improving public services, and the use of case studies to move these endeavors forward. They emphasize the ways in which case studies have become salient tools for frontline staff whose everyday work is trying to solve complex development challenges, especially those pertaining to the implementation of policies and projects, and how, in turn, case studies are informing a broader turn to explaining outcome variation and identifying strategies for responding to complex challenges and ultimately seeking to enhance development effectiveness. The chapter discusses seven qualities that make a case useful to practitioners and then offers reflections on how to use cases in a group context to elucidate core ideas and spark innovation.

Widner reflects on what she and others have learned about gathering reliable information from interviews. Case study researchers usually draw on many types of evidence, some qualitative and some quantitative. For understanding motivation/interest, anticipated challenges, strategic choices, steps taken, unexpected obstacles encountered, and other elements of implementation, interviews with people who were “in the room where it happens” are usually essential. Subject matter, proximity to elections or other sensitive events, interviewer self-presentation, question sequence, probes, and ethics safeguards are among the factors that shape the reliability of information offered in an interview. Widner sketches ways to improve the accuracy of recall and level of detail, and to guard against “spin,” drawing on her program’s experience as well as the work of survey researchers and anthropologists.

Pavone analyzes how our evolving understanding of case-based causal inference via process-tracing should alter how we select cases for comparative inquiry. The chapter explicates perhaps the most influential and widely used means to conduct qualitative research involving two or more cases: Mill’s methods of agreement and difference. It then argues that the traditional use of Millian methods of case selection can lead us to treat cases as static units to be synchronically compared rather than as social processes unfolding over time. As a result, Millian methods risk prematurely rejecting and otherwise overlooking (1) ordered causal processes, (2) paced causal processes, and (3) equifinality, or the presence of multiple pathways that produce the same outcome. To address these issues, the chapter develops a set of recommendations to ensure the alignment of Millian methods of case selection with within-case sequential analysis.

Andrew Bennett help us think about what steps are necessary to use case studies to identify causal relationships and draw contingent generalizations. He suggests that case study research employs Bayesian logic rather than frequentist logic. “Bayesian logic treats probabilities as degrees of belief in alternative explanations, and it updates initial degrees of belief (called ‘priors’) by using assessments of the probative value of new evidence vis-à-vis alternative explanations (the updated degree of belief is known as the ‘posterior’).” Bennett sketches four approaches: generalization from ‘typical’ cases, generalization from most- or least-likely cases, mechanism-based generalization, and typological theorizing, with special attention to the last two. The study of deviant, or outlier, cases and cases that have high values on the independent variable of interest (theory of change) may prove helpful, Bennett suggests, aiding the identification of scope conditions, new explanations, and omitted variables.

Margaret Levi and Barry Weingast focus on a particular type of case in which the subject is an outcome that results from strategic interaction, when one person’s decision depends on what another does. “A weakness of case studies per se is that there typically exist multiple ways to interpret a given case,” they begin. “How are we to know which interpretation makes most sense? What gives us confidence in the particular interpretation offered?” An analytic narrative first elucidates the principal players, their preferences, key decision points and possible choices, and the rules of the game. It then builds a model of the sequence of interaction including predicted outcomes and evaluates the model through comparative statics and the testable implications the mode generates. Most analytic narratives model situations as extensive form games. However, although game theory is useful, there is no hard rule that requires us to formalize. In this kind of case study, the findings do not generalize to other contexts, but instead point to the characteristics of situations to which a similar strategic logic applies.

Political scientist Cammett considers the use of positive deviant cases – examples of sustained high performance in a context in which good results are uncommon – to identify and disentangle causal complexity and understand the role of context. Although the consensus view on the role of deviant cases is that they are most useful for exploratory purposes or discovery and theory building, Cammett suggests they can also generate insights into the identification and operation of causal mechanisms. She writes that “analyses of positive deviant cases among a field of otherwise similar cases that operate in the same context … can be a valuable way to identify potential explanatory variables for exceptional performance.” The hypothesized explanatory variables can then be incorporated in subsequent quantitative or qualitative studies in order to evaluate their effects across a broader range of observations. The chapter discusses how to approach selection of positive deviant cases systematically and then works through a real example.