The fundamental output of causal mapping is a database of causal links. If there are not too many links, this database can be visualised "as-is" in the form of a causal map or network. But usually there are too many links for this to be very useful, so we apply filters.
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.
As explained on the Causal Mapping website:
"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 figure below shows a map from the application Causal Map, showing coded causal statements for a project that provided farmers with agricultural training and advice in order to increase crop yields. The map has been filtered to show only outcomes downstream of the influence factor ‘Agricultural training and advice’. Numbers shown indicate how many times the links were mentioned across all interviews.
Source: BDSR, 2021, p 4
The logic of QDA: you’ve done your analysis, now what?
How does causal inference work in a causal network?
(An example of kind-of qualitative causal logic, with a focus on groups: @castellaniCasebasedSystemsMapping])
Evaluators can break the Janus dilemma and make the best use of causal maps in evaluation by considering causal maps not primarily as models of either beliefs or facts but as repositories of causal evidence. We can use more-or-less explicit rules of deduction, not to make inferences about beliefs, nor directly about the world, but to organise evidence: to ask and answer questions such as:
These questions may depend on a somewhat hidden assumption: that all the causal claims (the links) come from a single context. Such as, in most cases, when all the claims are all agreed on by a group as in participatory systems mapping (PSM). For example, when we wrote "which factors are reported as being causally central?", can we really answer that by simply checking the network? From:
Image from Wikipedia by Jayarathina - Own work, CC BY-SA 4.0.
[[882 Granularity, generalisability and chunking are coding problems for causal mapping too]] [[Source clustering]]