What do we even mean by “outcome”? A factor might have a lot of incoming links, so it is often mentioned as the consequence of something, but it might also have a lot of outgoing links, so perhaps plays more of an intermediate role. One way to answer this question is to calculate the “outcomeness” for each factor: the number of citations of its incoming links divided by the total number of times it was cited.
High outcomeness means that a factor was often at or near the end of causal stories. A table of factors listed in descending order of outcomeness can help for example, to better understand the perceived impacts of your project or implementation.
Causal mapping looks for linearity first▸
Causal mapping most often looks for linearity first, while of course being on the lookout for feedback loops and circular shapes. Whereas most systems approaches do the opposite.
Can you spot a complex system when you see one?
Version 1
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The network pictured above, even though it is quite small, looks pretty tangled. We're not going to fully understand it, so we'd better get out our tools for dealing with complexity? But wait, look at the boring, old-fashioned hierarchy below.
Version 2
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Did you spot that they have exactly the same structure? Now it is easier to see that it is just a hierarchy. D, E and F have one contributor each, whereas G and H share I, J and K as contributors, and feed only into B, whereas D, E and F all feed into both B and C, which feed into A. Easy. Nothing which should be too hard to predict, no balancing feedback loops.
"Complex" and "System" are very buzzy buzz-words at the moment. We should check we don't throw them around too much without thinking. I'm just reading Moore, Parsons and Jessop in the American Journal of Evaluation. They quote Magee and de Weck (2004) who define complex systems as systems "with numerous components and interconnections, interactions or interdependence that are difficult to describe, understand, predict, manage, design, and/or change." Well yes, kinda. But what if you find a system difficult to describe, etc, just because you didn't look hard enough?
Yes, causal maps are just concept maps with only one type of connector, and that connector means "... causes....". Whereas concept maps can have any type of connector you like. Historically, causal maps come from concept maps.
Laying out causal maps is a challenge! Most folks from the systems tradition like swirly circular layouts which make them look like everything is one big feedback loop. If there is a more linear structure, we recommend showing that linear structure.
Outcomeness is a useful measure of whether a factor is more of an outcome or a driver#
Causal Map primarily uses the Graphviz DOT layout engine which does an amazing job of teasing out such a story if there is one. Generally speaking, the "drivers" will be on the left and the outcomes will be on the right, but at the same time trying to maintain readability and avoid the links crossing over factors or over each other. Which is always a compromise. For this reason we also usually use "outcomeness" colouring for the factor backgrounds, which represents the proportion of all the factor's links which are incoming links. So normally factors on the right are darker, except where Graphviz has had to reposition some of the factors for readability. So, does it look like a ToC? Of course that depends on what you expect a ToC to look like and famously there are no standards for that.
If the causal map is more or less neatly hierarchical, then our map will reflect that nicely and therefore "look like a ToC" but of course that's rarely the case.
We often see that for reports, folks often take the original causal map and get a designer to redraw them anyway to match the report styling etc.
In the upcoming version 4 of Causal Map, there is a more interactive style of map where it's possible to drag the factors around to put them just where you want them.
