Three different ways to use causal mapping. The workflow is similar for each: import interviews and/or reports and code them (identifying all the causal claims, where someone says that one thing influences another) manually or with AI.
General social research
What do people think, and what drives what?
Gather open accounts on any topic and map the causal claims people make. No fixed framework: the drivers emerge from the material, feedback loops and all.
flowchart TD
A[Divisive posts] --> B[More engagement]
B --> C[Algorithm amplifies]
C --> D[Polarisation]
D --> A
D --> E[Distrust in institutions]
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class A,B,C,E node;
class D out;
A feedback loop driving polarisation, with a downstream branch.
Testing a theory of change
Does the evidence back your theory of change?
Build a theory of change inductively from reports and interviews, or apply a fixed codebook from an existing theory, or mix the two approaches. Either way the map can also surface outcomes you never expected. Behind each link are multiple quotes from multiple sources.
flowchart TD
T[Training delivered] --> S[Skills put into practice]
S --> O[Outcomes improve]
U["⚡ Unexpected: peer mentoring"] --> O
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class T,S,O step;
class U surprise;
Validating a theory of change
As the skeleton for an entire evaluatio
Run the whole evaluation from one coded evidence base
Import all your material, code it once, and use the map as the backbone of the study. Express each evaluation question as one or more filters applied to the complete map. Then read the answer off the evidence.
flowchart LR
I[Intervention] --> PA[Pathway A]
I --> PB[Pathway B]
PA --> O1[Outcome 1]
PB --> O1
PB --> O2[Outcome 2]
X[Other factors] -.-> O2
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class PA,PB,O1,O2 node;
class I src;
class X alt;
Questions you can answer