Section 1 What’s in this guide?

A rough guide to the nascent theoretical basis behind the causal map app.

We’ll look at:

  • what is causal mapping?
  • the need for the app
  • a guide to the app
  • tips on coding

There will also be general materials on harder issues in the coding and meaning of causal networks…

In the first, theoretical, part of this guide I’ll talk about causal maps in general, what the different types have in common, how we can encode causal information in them and how to extract it again.

In the second, practical, part I’ll make some concrete suggestions for best practices in creating and presenting causal maps. It will be based on our app which we hope encapsulates this approach, but it should make sense however you are creating your maps.

Why a new tool? There are already plenty of tools for storing and presenting causal structures, primarily of two types

  • graphical e.g. Kumo
  • theoretical e.g. dagitty.

What we lack are tools of either type which help us to construct

  • models which include other people’s models. These are really important if we want to model the behaviour of people, groups or systems which have their own implicit or explicit Theories of Change
  • different versions of the same or overlapping models, e.g.
    • different stakeholders’ (maybe partial) information on the same causal topic
    • different editions or versions of a Theory of Change.