Influential ‘realist’ social scientist Ray Pawson has developed a handy, draft guide to causal explanation called ‘Causality for beginners’, which examines causality and the conduct of social research. He compares and contrasts three strategies:
- Successionists who examine the associations between variables, especially the outcome/effect variable (the ‘dependent variable’ [Y]) and other explanatory variables (the ‘independent variable(s) [X]) with a uniformity depicted by a causal arrow (X -> Y). Variables are “considered the vital causal agents”. Regularities or associations or correlations are viewed as providing “the basic building blocks of explanation”. Sucessionists often search for vital causes or seek to establish causality through experimental manipulation (e.g. randomised controlled trials). As such experimental design-based methods tend to be used.
- Configurational causation: according to configurationists “what allows for and produces change is the particular configuration of attributes within the system”. The “basic atom of inquiry is often referred to as the ‘attribute’ or ‘condition’… these attributes have the causal powers to condition what follows, just as we might say that weather conditions influence the style of rugby that can be played, or that market conditions shape the number of people willing to invest”. Pawson quotes Charles Ragin who elaborates the approach as follows: “The basic idea is that a phenomenon or change emerges from the intersection of appropriate pre-conditions – the right ingredients for change”. In terms of research, configurational research begins with “the creation a family of equivalent (but not identical cases)” and then seeks to understand “the consequences of their similarities and differences”; and
- Generative causal logic (mechanism-based) focussed on mechanisms, contexts and their outcomes. Pawson outlines three key elements: 1) an interesting, puzzling, nascent outcome pattern pricks our imagination; understanding the incipient regularity is a matter of postulating 2) the mechanisms that might have brought it about and 3) the contexts which sustain the action of the mechanism. Identification of mechanisms begins as hypotheses about the choices and reasoning of relevant actors. Regarding the role of context, Pawson elaborates as follows: “causal relationships only occur when a generative mechanism triggers. Discovering the explanatory mechanism in action, however, is only half the battle because the connection between its operation and the occurrence of the intended outcome is not fixed. Rather, outcome patterns are also contingent on context.” In summary:
“Causal explanations are propositions. The propositions explain by showing that a mechanism (M) acting in context (C) will generate outcome (O). These CMO propositions are the starting point and end product of investigation. Research commences with hypotheses attempting to explain a puzzling outcome pattern by postulating how its contours might be explained by the constrained choices which have operated within a substratum of contexts. Empirical inquiry will then go on to fine tune the understanding of the precise operation and scope of the Cs, Ms and Os. Causal explanation builds under such an incremental process of revision.”
A key difference is whether explanation is located in the variables and ‘attributes’ (in the case of successionist and configurational causation), or whether causal powers are “understood as ‘potentials’ or ‘processes’ inherent in the system studied” and processes of reasoning (in the case of generativists). Generative explanation is also pursued by creating and testing theories.
Pawson goes on to compare and contrast these models and argues the generative account provides a superior basis for building enduring, cumulative causal explanations and empirical generalisations. If your research involves assessing and/or making causal claims the assessment and recommendations are well worth a read (click here for more info).