📚 Context

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In this section, we’ll look at “Context” as it appears to us at Causal Map when we do actual (qualitative) causal mapping: taking causal claims which real-life stakeholders actually make and trying to encode them in as systematic a way as possible. We believe that doing qualitative causal mapping is a really good testbed for theoretical ideas in evaluation and social science: do they fit with what people actually say?

So, how do we code this in causal terms: “When enough oxygen is present, a spark will always cause a flame”? (How) can we distinguish between a context like Oxygen and just some causal factor like Flame? How do we encode a context in a causal map?

We can draw a line from Spark to Flame, but what do we do about Oxygen? Drawing a line also from Oxygen to Flame doesn’t seem to capture the context-ness of Oxygen.

In general terms this diagram is ok.

But we know the relationship between Spark and Oxygen to Flame is a partial function:

We know how Spark controls Flame only given Oxygen, but not when there is no Oxygen. This suggests that we cannot deal with a contextual factor as an ordinary causal factor. It is a special one which causally enables causal link(s) between other factors.

This lack of information about what happens in the absence of the contextual factor means that it acts like a sufficient condition for the causal relationship. (The defining characteristic of a sufficient condition is that no claim is made about what happens in its absence, just as the defining characteristic of a necessary condition is that no claim is made about what happens when it is present (only when it is absent).

Inside the context Oxygen, Spark is a necessary and sufficient cause of Flame (or so our respondent tells us). Outside the context C, Spark never makes any difference to Flame (or, we have no information about the effect, which isn’t the same thing, but it doesn’t matter here.)

Perhaps it is this very absence of information about what happens with no Oxygen which makes Oxygen feel more like a context rather than an ordinary causal factor. You can’t see that in this first diagram.

Of course there are other things which you could mention as part of the context: there is fuel, it’s dry, etc.

There are many ways to encode contextual information in a Causal Map; here we suggest storing the information inside each link.

At the moment, you can encode context in the Causal Map app simply by creating a hashtag for it. You can use a family of hashtags by using some characters in common, like “Context:”, like this:

You can also search by and filter for contexts

✨ Transforms Filters: Include or exclude hashtags, so you can show a map only in the context of oxygen or without that context.