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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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Task 2 – Introduction
Minimalist coding does not help with blockers and enablers
Our approach is minimalist – we code only bare causation
Our approach clearly distinguishes evidence from facts and does not automatically warrant causal inferences
Our approach is minimalist – factors are not variables
1a A minimalist approach to coding helps capture what people actually say
1b A minimalist approach to coding makes aggregation easier
1c A minimalist approach to coding does not code absences
Our approach is minimalist – we do not code the strength of a link
In a causal mapping dataset there is no need for a special table of factors
Factor labels – a creative challenge
Factor label tags – coding factor metadata within its label
Factor labels – semi-quantitative formulations can help
Factor labels – do not over-generalise
Coding with and using link metadata
Link metadata – Sentiment
Link metadata – Time reference
Link metadata – quality of evidence
Research on LLMs' ability to detect causal claims
A formalisation of causal mapping
🌻 Our approach clearly distinguishes evidence from facts and does not automatically warrant causal inferences
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