What is Causal Mapping? 


Causal mapping maps people’s understanding of what they think are beliefs about, or evidence for, causal connections.
Peoples’ narratives and reflections about their experiences provide the qualitative data that can be coded and displayed as maps both to present the cognitive structures (mental models) of individuals and groups and to support further exploration to understand actual causal connections.
More generally, causal mapping is used to organise sets of evidence from different sources (not just people but also documents) about causal links between multiple different causal factors.
Causal mapping is a way of making sense of a large amount of text to answer questions about what people think caused what by building links between different factors. These factors can include different kinds of and various outcomes, both intermediate and long-term and inputs. Mapping the chains of results and their linkages builds pictures of causal pathways that show the intermediate steps and connections between steps.
Causal mapping is designed for the analysis and visualisation of qualitative data about causal links. It can be used to test an existing theory of change or create collective empirical theories of change about how a program is working based on stakeholders’ experiences.
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Source: From narrative text to causal maps: QuIP analysis and visualisation, Bath Social & Development Research Ltd 2021 p4


The results of ordinary qualitative research on texts is usually just more text, perhaps with some tables of frequency of occurrence or co-occurrence of particular themes for particular respondents and a chart or even a network graph to present these results.
Causal maps, on the other hand, are not additional presentations of additional analyses but are the main product of qualitative causal mapping. They are relatively intuitive and easy to understand.


A global causal map resulting from a research project can contain a large number of links and causal factors. By applying filters and other algorithms, a causal map can be queried in different ways to answer different questions, for example to simplify it, to trace specific causal paths, to identify significantly different sub-maps for different groups of sources, etc.


The original quote on which each causal link is based is stored within the link itself. That means that at every stage of causal mapping, it is possible to directly to return to the original story, in the original context.


Causal mapping also encourages reanalysis of existing narrative data which is often gathered but left unanalysed. It is highly suited to online use, e.g. gathering narratives via online interview, email, questionnaire etc., reducing airmiles and unnecessary travel.


Causation - a taboo?

Gone are the days when we could think of data or information as primarily about numbers. Many of us who are involved in understanding the social world and evaluating interventions within it spend much of our day understanding, presenting, manipulating and caring about people’s mental models of the world.
  • Causal information is primary information. It isn’t something which exists only virtually as a potential conclusion on the basis of observations of non-causal variables. There is a fact of the matter about what causes what, just as there is a fact of the matter about the number of people who attended a training course.
  • Parallel to that, humans’ perception of causation is primary, as primary (and fallible) as our perception, say, of colour. All the things which we know, or think we know, about our world – from the colour of that dress to the way the wind shakes the trees – have already been through a lot of cognitive processing, and none of it is ‘secondary’. So, when we ask stakeholders the “why question” (what causes what), we are not asking them about what they deduce from their (non-causal) observations in the way we might as scientists or researchers. No, we are asking them about what causes what based on their underlying understanding of the causal structure of their world, which they have pieced together in a number of different ways.
Causal mapping is the process of constructing, summarising and drawing inferences from a causal map, and more broadly can refer to sets of techniques for doing this. While one group of such methods is actually called “causal mapping”, there are many similar methods which go by a wide variety of names.

History of causal mapping

(from Wikipedia)
The phrase “causal mapping” goes back at least to Robert Axelrod,* based in turn on Kelly’s personal construct theory .*  Causal mapping in this sense is loosely based on "concept mapping" and “cognitive mapping”, and sometimes the three terms are used interchangeably, though the latter two are usually understood to be broader, including maps in which the links between factors are not necessarily causal: these maps are therefore not causal maps.
Literature on the theory and practice of causal mapping includes a few canonical works* by Axelrod, Eden, Ackermann, Laukkanen, Hodgkinson and others, as well as book-length interdisciplinary overviews, ** and guides to particular approaches.*