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What are the advantages of Causal Mapping? 


Causal mapping aims to directly understand and collate the causal claims which people make in narrative (and other) data rather than trying deduce causal connections using statistics or other methods. It starts with what people actually say in real-world contexts and does not rely on heavily pre-structured question formats. Urgent, unexpected, and unwelcome information is treated at face value.

The analyst does not need to have any preconceived conceptual framework; types of causal claims
are identified in the data inductively and iteratively. This is a partly creative process, however the decisions made by the analyst are transparent as the underlying text is always available.

At least some of the boundaries of causal mapping research are set by the respondents, not the researchers; what are we going to talk about? What are we not going to talk about?

It is also possible to conduct causal mapping more or less deductively.


Causal maps work on two levels. On one level, they are presentations of individual and shared cognitive structures, the maps “in people’s heads” which we need to know about if we want to understand, predict and influence behaviour. On the other level, they are putative, fallible maps of the actual causal world: how things work. Like all other research results, these maps may be wrong, but they usually contain at least some truth. At Causal Map we take a realist stance on both of these levels: the maps in people’s heads are real, and the causal world, made up of many causal links between causal factors, is real too. This is core to the QuIP methodology as explained in the book – Attributing Development Impact:

…attribution claims underpinning the QuIP do not require a control group, nor indeed variation in exposure to the intervention across the sample of respondents interviewed. Rather, causal claims rely on the integrity of ‘within-case’ statements made by respondents themselves

-Copestake, Morsink & Remnant, 2019


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. With certain assumptions, it is possible to ask and answer questions like “which is the largest influence” or “which is the most positive effect”.


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. 


A causal map is a directed graph which is intended to model causal relationships, in which the factors (nodes, elements) are linked by arrows which mean that the factor at the start of the arrow causally influences the factor at the end of the arrow. This is a broad definition which covers many different existing ‘paradigms’ of causal modelling. In general, the individual causal connections in a causal map may be based on information from more than one source.

This will sound familiar to anyone who has read about or used the Qualitative Impact Protocol – QuIP, with its emphasis on causation and attribution. Indeed, Causal Map could be defined as a daughter of QuIP, developed and nurtured with the benefit of the real-world experience derived from Bath SDR’s work on QuIP, but designed to flourish in a much broader context.

Many kinds of diagram already in use by social scientists and programme evaluators come under this heading:

  • Theories of Change for a project or programme, even (in a very restricted sense)

  • Logical Frameworks

  • Results Frameworks

  • Programme theories in theory-based evaluation

  • Fuzzy Cognitive Maps

  • Systems Diagrams

  • DAGs, as promoted by Judea Pearl

  • Structural Equation Models

  • Bayesian belief networks (as long as they are meant to express explicitly causal relationships rather than paths for probabilistic inference

  • Diagrams used in Realist Evaluation and sometimes Outcome Harvesting

  • (Comparative) Cognitive/Causal Mapping in the narrower sense

  • Causal Maps as constructed in QuIP studies


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 causal structures (and even models of other people’s causal structures).

  • 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.

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