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Glossary
Causal mapping – overview
Task 1 – Gathering causal mapping data
Task 2 – Causal coding – minimalist style
Task 2 & 3 Key ideas and conventions
Task 3 – Answering questions – General
Task 3 – Answering questions – Individual questions
Causal mapping in evaluation
Causal Mapping as QDA
Causal Map app and alternatives
Deductive coding with AI
Inductive coding with AI
Improving rigour in the use of AI in social science
Qualia
Case studies
Getting philosophical
AI and the wider world
Finally
Causal Map App
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Individual questions – introduction
Individual views – How does the system work according to individual sources?
Summarising – How do the sources claim that the system works, in summary?
Vignettes – What is a typical source and what is their story?
What are the narratives behind a specific link?
Which factors and links were most frequently mentioned?
Which factors and links are mentioned by the most sources?
Main outcomes. Which factors are mentioned most often as outcomes?
Main drivers. Which factors are mentioned most often as drivers?
Splitting by groups. Are different groups involved in different ways?
Comparing groups – What factors or links were mentioned more by some groups than others, in the same map?
Identifying groups – Are there different subgroups within the data?
What are the emerging or unexpected factors?
Does the evidence support your theory of change?
Assessing systems change
Sentiment – Which changes are perceived as most positive or negative?
Focusing on specific factors. What influences and outcomes are connected to a specific factor?
Looking downstream. What are the direct and indirect consequences of one or more factors?
Looking upstream. What are the direct and indirect influences on one or more factors?
Names of tables and fields
Path tracing – How do one or more causes affect one or more effects, including indirect pathways?
Source tracing – What are the consequences of one or more factors, looking only at stories told in their entirety by individual sources?
Robustness – How robust is the evidence for that X influences Y?
Counting and comparing influences
Properties of the causal map – Which factors are reported as being causally central or causally peripheral?
Properties of the causal map – What is the overall structure of the network?
Properties of the causal map – Are there leverage points?
Properties of the causal map – Are there feedback loops?
Combining questions
Tribes. The most relevantly different subgroups in your data (by causal story)
Showing group data as custom link labels on the map with optional significance test
🌻 Source tracing – What are the consequences of one or more factors, looking only at stories told in their entirety by individual sources?
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