Granularity, generalisability and chunking are coding problems for causal mapping too

💐 Causal Mapping: A garden of ideas

(⚒️ Work in progress!)

Glossary
- 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
- Causal mapping with kumu
Deductive coding with AI
- Intro – deductive auto-coding
- The deductive autocoding prompt
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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Chapters▼

Chapters

Home 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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Pages▼

Pages in this chapter

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

🌻 Granularity, generalisability and chunking are coding problems for causal mapping too

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The transitivity trap