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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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Intro
Causal mapping for outsiders
A causal map consists of multiple links where a link from X to Y means someone believes X influences Y
Causal mapping helps make sense of many causal claims from many sources
Causal mapping starts from what people actually say and what they do not say
Causal mapping has been used for over 50 years in many disciplines
Do use causal mapping when you have large numbers of claims from multiple sources, and more open research questions
Do not use causal mapping if you have limited data or want precise models or specific causal links
Causal mapping approaches differ in application, construction, analysis and how they deal with multiple sources
Causal mappers believe that humans are good at thinking in terms of causal nuggets
Causal mappers believe that humans are the best detectors of causation
Causal mapping is part of the qualitative branch of the new causal revolution
Causal mapping differs from related approaches - epistemic, less predictive, unsophisticated, many links, many sources, unclear boundaries
Causal mapping has three tasks – gathering, coding and analysing data
Task 1 – Gathering narrative data
Task 2 – Coding causal claims as causal qualitative data analysis
Task 3 – Analysing data, Answering questions
Strong evidence for a link is not evidence of a strong link
Causal mapping is easier if we are realist about causation
Causal mapping is good at coping with messiness and complexity
Granularity, generalisability and chunking are coding problems for causal mapping too
🌻 Causal mapping differs from related approaches - epistemic, less predictive, unsophisticated, many links, many sources, unclear boundaries
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