Some methods related to causal mapping and even some forms of causal mapping itself are not interested in individuals' different causal perspectives: they are primarily aimed at reaching a consensus, expert map straight off without first recording individual viewpoints and attempting to combine them.

You have a causal map with lots of links from an intervention to a final outcome. It's a really impressive chain.

We have often processed data gathered specifically for evaluation purposes using "causal back-chaining". But we often process secondary data which was not specifically intended for causal coding.

When gathering primary data, the way in which questions are asked influences the meaning of the maps and their links. For example, in the QuIP, (Copestake et al., 2019b) respondents are asked to identify causes of changes, then causes of the causes and so on. This means that most of the factors are already as changes in something, such as ‘an improved harvest’ or ‘reduced hunger’). This has implications for how positive and negative statements are combined, as discussed later.