Section 7 We need rules about how to encode causal information in a causal map (and decode it again)
Causal maps are supposed to encode causal information. But how?
How should we encode -– capture, write down, picture -– causal information? How should we convert causal information -– whether gathered from narratives or experiments or experts -– into a causal map?
The reverse question:
How should we interpret a causal map when we see one?
These section will try to answer these questions in a reasonably general way, i.e. not just for specific kinds of map.
We learned in school how to write down and manipulate numerical information. But not causal information. We need to agree, and teach one another, how to write down and manipulate causal information too.
For the moment, we are not at all interested in which particular medium -– for example, sentences as opposed to diagrams -– we want to use to encode causal information, because we want to understand the rules which should govern the use of the symbols in any such encoding. (Spoiler: in fact we will be using diagrams containing arrows between boxes, basically, for this task.)
A social researcher or an evaluator might be set the task of encoding the causal information in some written or spoken information, say from an interview, and for that they will need symbols or conventions which make the causal information clearer.
We’d like to perhaps be able to use different symbols so we can specify these links more precisely, and add a corresponding legend to the map. But what symbols do we need? Some kinds of causal map already have their own sets of symbols and conventions, like SEMs. Do all the different kinds of causal map, like those in the list, all need to have their own legends? Can some be shared? The problem of what symbols to use is certainly a problem for the first three.
If we have to convert fragments of causal information expressed in other ways, like narrative testimonies from stakeholders about some domain of interest, into causal maps -– a task we will refer to as “coding” -– we need to know how to do that, what symbols and conventions we need to use to encode as much of the relevant causal information as we can.
So we have a challenge to establish rules, conventions, for constructing causal maps which makes their meaning transparent.
7.1 How do causal claims work within ordinary language?
Of course, people speak and write sentences which contain causal information all the time. But it isn’t easy to see exactly what information is contained in such sentences, because in natural language we have a very messy and heterogeneous patchwork of ways of talking about causation: dozens of half-complete systems for encoding different variants of causal information, some of them suffused with ideas like blame and responsibility. Just as language contains overlapping, only partially consistent and mostly incomplete, ways of talking about time, and about space, and about quantity. Mathematicians and philosophers have nevertheless managed to systematise them in ways which are mostly satisfactory.
Causal language is not one system. But in order to be able to work with causal maps, we need to agree on a single set of rules about encoding and decoding causal information in them; and they should work as a good enough approximation to the ones we find naturally occurring in language.
7.2 Understanding the elements of a causal map by agreeing how to make deductions with them
Causal arrows are not conduits for a mysterious force caused “causation”. What flows down the arrows, so to speak, are actual forces - magnetism, peer pressure, greed, whatever. The arrows are the conduits for these actual forces: the form, not the content.
We will not get anywhere if we try to understand the meaning of a causal arrow, or how to use it, by using synonyms for “cause”.
Instead we will learn its “meaning” in precisely the way we learn the “meaning” of arithmetical symbols like + for addition: by learning the rules by which we may draw inferences between sentences containing such symbols.2
I claim that this problem about meaning can be solved by agreeing on how to reason with causal maps, to do causal inference. The causal map app needs to have the right rules for causal inference in order to be able to bring the maps alive -– so that we can ask them questions like “does variable B have more influence on E than variable C?” or give them instructions like “Hide all the variables which only have a small influence on E.”
When we learn the meaning of + in school, words don’t help us much, we learn by making deductions; we learn that from
x = 2 + 3
we can deduce, for example,
x = 5
according to certain rules. We could call them “inference rules”.
But in this guide, we aren’t dealing with equations, we are dealing with causal maps. In order to be able to explicate what the elements of causal maps mean, we need to agree on inference rules for causal maps.
As far as possible, we will try also to identify symbols and conventions which fuzzily capture the fuzziness, incompleteness, ambiguity etc of the way people talk about causal relationships and of the supposed causal relationships themselves.
If we were doing formal logic, we could say that we want to establish the axioms and theorems for sentences (or diagrams) involving the causal arrow.↩