Chapter 9 The elements of a causal map

9.1 Variables and propositions

9.2 Variables, types of variable, and contrasts

We will need to distinguish different types of variable. Here are a few key types:

  • ◪ continuous, limited variables like percentage, usually specified as going from zero (“nothing”) to 1 (“everything”). I call them “lo-hi variables” but I would love to hear of a better name. Can also be expressed as a %, because this is more familiar to people. We do not necessarily strictly understand this number between 0 and 1 as a proportion.
    • So we can say, “confidence in the President is around 20% whereas for the former President it was around 50%” or “the project was performing at about half of its potential” even if we don’t make it clear what the numbers mean exactly.
    • The point is our respondents will use such language which we need to reproduce, and also that when coding and then combining causal fragments, we sometimes have to commit to expressing a non-numerical claim with a rough number.
    • These ranges might also be tied to some kind of empirical distribution, so “Income from farming = .95” would mean it is in the middle of the top 10%, etc.
  • ◨ false/true variables like “the project is implemented (yes or no)”
  • ◢ continuous, unlimited variables like height, income
  • ..… and various others, see xx.

When actually coding causal maps, we will mostly use ◪ variables, as ◨ variables are a special case of them, with just the levels 0 (no) and 1 (yes).

9.3 Causal thinking is essentially contrastive thinking

Variables contrast the actual (or imagined) state of things with possible alternatives.

all causal claims intrinsically have counterfactual meaning. (I should probably have said “contrastive” rather than “counterfactual” as the latter would more strictly only refer to past things which can no longer be changed, but this doesn’t matter here). If A claims that contrails causally influence the weather, and in particular that if if there are a lot of contrails, the weather will be worse, but has no opinion at all about what the weather would be like (controlling for any other influences) if there were fewer contrails, in particular doesn’t claim it would be any better, then they haven’t understood what “causal influence” means. In other words, I am suggesting that a useful and comprehensive general framework for causal networks in social science can start by treating the elements as variables in a broad (and mathematical rather than statistical) sense, i.e. as things that can / could be / could have been different.