Our approach is algorithmic

💐 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

Tasks 2 & 3 – Introduction Our approach is algorithmic Simplification - co-terminal link bundles Hierarchical coding Causal mapping looks for linearity first ➕➖ Opposites Context Plain coding Table features – Statistical tests of group differences Reporting global and local network statistics Simplification - factor and link frequency Simplification - hierarchical zooming

🌻 Our approach is algorithmic

All the pioneers of causal mapping used different algorithms to simplify, query and synthesise their data.

What we add: TODO

Backlinks

! Causal pathways -- when is a pathway not just a link