Causal mechanisms are an explanatory rather than predictive tool used to unpack the “black boxes” defined by empirical generalizations common in policy research. Specifically, mechanisms can be frequently occurring and easily recognizable causal patterns that are triggered under generally unknown conditions and usually with indeterminate consequences. This approach is appropriate for public administration, public policy, and governance research because it is often based on small N between-case and within-case studies. In testing hypotheses associated with causal mechanism, process-tracing methods are often used.
Issues of causality are infrequently discussed within the public policy and public management disciplines. Positivism and interpretivism are the two most popular approaches. The underlying notion of causality underpinning positivism is one of simple event regularities; that is, there are constant conjunctions of empirically observable, measurable, and predicable events. In contrast, interpretivism is based on the idea that reality is subjective and socially constructed. This view of the social world is constituted by human meaning and sees the main task of research as exploring and understanding subjective human viewpoints. Here, the importance of causality is minimized because all explanations are merely personal or group viewpoints and have to be judged.
However, both of the approaches can be limited for policy researchers who are interested in explaining phenomena such as the policy process because they are often “black boxed.” The causal mechanism approach has become a popular third perspective used to identify the specific details about the cause-and-effect relationships between policy-maker attention to policy problems and their receptivity to policy solutions. When the nature of the mechanism is understood, then it becomes possible to apply rigorous methods and make inferences.
Transformational mechanisms are those in which individuals, through their actions and interactions, generate intended and unintended outcomes. Third, researchers need to be aware of the temporal nature of mechanisms which includes the time horizons of both the mechanism and outcome (Beach and Pedersen 2013). For example, some slow-moving causal processes result in a threshold event resulting in a sudden change. In the policy and public management, there are many examples of mechanisms that fit these high-level albeit somewhat abstract mechanisms. It is also critical that more compelling and measurable causal explanations can only be realized if mechanisms are disaggregated from a high level of abstraction, which they label as “processes,” “mechanisms-as-type,” and “mechanisms-as-example” which explains why things happen. Making a measurable causal claim and describing how things happen require attention to “mechanisms-as-cause” and “mechanism-as-indicators (Falleti and Lynch 2008).” Thus, mechanisms are often nested hierarchies that contain “lower level entities, properties, and activities” that “produce higher level phenomena” (p. 13). Machamer et al. (2000) borrow from molecular biology and find that mechanisms “bottom out in descriptions of the activities of macromolecules, smaller molecules, and ions” (p. 14).
Mechanistic-Based Policy Analysis
Process-based analysis is critical because “when the procedural sides of a policy making or decision-making process have been thought through properly, it will greatly increase the likelihood of substantive problems being resolved” (Mayer et al. 2013, p. 181). The policy research and analysis communities should be interested in testing policy mechanisms so as to measure policy-oriented causal inferences and explain outcomes in more practical terms. Policy and public management research is often based on small N between-case and within-case studies. When the mechanisms are known, analysts can collect diagnostic evidence, theorize variables and empirical proxies, and test hypotheses which then provide a narrative explaining how a particular outcome or set of events came about (Kay and Baker 2015).
Testing hypotheses can be achieved through rigorous methods such as process tracing and qualitative comparative analysis (QCA) which are two popular methods for capturing causal mechanisms in action. Rather than testing for probability, analysts consider necessary and sufficient conditions as their criteria (Kay and Baker 2015). Necessity refers to the situation that the outcome cannot be produced without a condition, whereas sufficiency refers to the situation in which a condition itself can produce the outcome without the help of other conditions. Process tracing can involve understanding a simplistic change of events related to a single phenomenon, the convergence of a number of conditions, or complex interactions causal factors (Meyfroidt 2016; Trampusch and Palier 2016). This is often achieved by converting narratives into mechanistic explanations. Beach (2017) argues that analysts can develop minimalist or systematic understandings of mechanisms. A minimalist approach considers only diagnostic evidence. A systematic approach explicitly unpacks the causal process and delves into understanding the empirical fingerprint that the mechanism makes and unpacking it into its constituent parts. In part, this can be a function of the variety of information sources collected (archival documents, interviews, reports, memos) that are accumulated over a given period of time (Charbonneau et al. 2017).
Beach and Pedersen (2013) identify three types of “process tracing”: theory testing, theory building, and explaining outcomes. Theory-testing process tracing is employed when a phenomenon X is causing outcome Y that is known but the mechanism is not specified. Since mechanisms are portable concepts, they can be applied by policy researchers to further elaborate the long-term nature of policy change. Alternatively, in theory-building process tracing, the relationship between X and Y is detected, but the researcher cannot identify the mechanism or when the outcome (Y) is known, but X is unknown. In both cases, the researcher develops a new mechanism. Beach and Pedersen (2013) describe Janis’ (2013) development of “groupthink” mechanism as the cause of the Bay of Pigs fiasco. Theory building would require considerably more time and effort than theory testing. In explaining-outcome process tracing, the outcome (Y) is known, but X is unknown, or the researcher is interested in fully explaining why X happened. In each type of process tracing, the analyst will develop a causal mechanism. The second step involves operationalizing the mechanism based on “observable manifestation” from different types of evidence. From collecting such information, the inferential weight of the evidence and the hypotheses can be assessed using four well-known tests that apply Bayesian probability (straw in the wind, hoop, smoking gun, and double-decisive tests) (See Van Evera 1997). These tests examine necessary and/or sufficient conditions for inferring evidence from the hypotheses that exist. The principles of certainty and uniqueness of the evidence reflect the necessary and sufficient conditions. The straw-in-the-wind test supports or weakens a hypothesis but does not exclude it. The smoking-gun test confirms the hypothesis but does not exclude other hypotheses. Hoop tests reject a hypothesis but do not influence other hypotheses. Finally a double-decisive test confirms a single hypothesis and disconfirms other rival hypotheses. Often researchers will be interested in comparing a number of cases, for example, comparing climate change policy in a number of jurisdictions. Qualitative comparative analysis (QCA) is popular approach which applies set theory and conceives cases as configurations of attributes. QCA examines thes necessary and sufficiency of configurations of conditions combined to generate outcomes and enable causal interpretation (Ragin 2014).
Causal mechanisms have been overlooked because they are usually hidden but are sensitive to variations in context. A toolkit equipped with well-elaborated mechanisms is not only useful for precision and depth to understand the generative processes of existing theoretical models but is also valuable for empirical research and enhancing policy-making decisions (Tranow et al. 2016). Thus, the policy scholar, scientist, and public official can benefit from deeper understanding of causal mechanisms. Policy scholars should take up the challenge and identify more specific mechanisms via process tracing in existing social science theories or develop new mechanisms. This may lead to improving, changing, or refuting the broad and sometimes vague assumptions about existing theories and frameworks. By doing so, academic researchers will be able to undertake richer empirical work.
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