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Causal mapping – overview
Task 1 – Gathering causal mapping data
Task 2 – Causal coding – minimalist style
Task 2 – Coding with AI
Task 2 & 3 – Extensions
Task 3 – Answering questions – General
Task 3 – Answering questions – Individual questions
Causal mapping in evaluation
Causal Mapping as QDA
Causal Map app and alternatives
AI in qualitative social science
How to – in the Causal Map app
Qualia
Case studies
For consultants
AI and the wider world
Finally
Causal Map App
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Task 2 – Introduction
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
A minimalist approach to coding helps capture what people actually say
A minimalist approach to coding makes aggregation easier
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
Causal mapping looks for linearity first
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 the ability of LLMs to detect causal claims
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Tags
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Tags
Bath Sdr
AgDevCo, Uganda
Concern Worldwide, Malawi
Diageo, Kenya
Fairtrade, Cote D'Ivoire
Feed the Children, Kenya
GirlEffect, Rwanda
Kantar Public
Mannion Daniels
OPM, Ghana, Mastercard
Opportunity International, Ghana
Pilot Universal Child Benefit Programme in Kenya, UNICEF Kenya
Power to Change, UK, 2020
Save the Children, Zimbabwe
UNICEF Innocenti. Qualitative Study of the Social Cash Transfer Programme in Urban Zambia
UNICEF study uses Causal Map to explore complex drivers of child work in India
Voscur
World Concern. Evaluating a Holistic Community Development Program with QuIP and Causal mapping
Case Studies
A workflow for collecting and understanding stories at scale, supported by artificial intelligence
Add Qualia to your next bid
AgDevCo, Uganda
AI-assisted causal mapping. Uncovering causal pathways with INTRAC
An M&E time machine. Using AI to measure changes in a system across a time period
Cactus Research, Kings College
Can AI accurately map causal claims - A validation study
Case study – our 'seamless stories' workflow in practice
Case study – Qualia asks about USA problems, again
Chartered Management Institute
Comparing a Fine-Tuned Model to an Engineered Prompt in the Context of Causal Connections in a Passage of Text
Concern Worldwide, Malawi
Creative Home Delivery Service, PSU
Diageo, Kenya
DUOC UC. Evaluating Gender Equity in STEM with AI-Driven Interviews
Everyone Counts, Covid Edition. Chapter 6, On the Front Line, Stories of the Volunteers
Exploring the Role of Social Protection in UK Asylum-Seeker Wellbeing Using Human Scale Development Theory
Fairtrade, Cote D'Ivoire
Feed the Children, Kenya
ForumFed. Strengthening Federal Governance and Pluralism in Ethiopia
GirlEffect, Rwanda
GYA, Global Young Academy
Include Causal Map and QualiaInterviews in your next bid
Kantar Public
Mannion Daniels
MK Institut, Mission und Kirche
Nepal Earthquake Federation-wide Meta-evaluation
OPM, Ghana, Mastercard
OPM, Tanzania
OPM, Zambia
Opportunity International, Ghana
Partner Ring, ACI, Australia
Pilot Universal Child Benefit Programme in Kenya, UNICEF Kenya
Power to Change, UK, 2020
Power to Change, UK, 2021
Save the Children, Zimbabwe
Strengthening OH with causal mapping
Tearfund
Thinking together within and beyond Communities of Practice
Together for Change, Solvacare
Tree Aid - Empowering Communities Through Forest Management in Burkina Faso
UNDP Chile
UNICEF Innocenti. Qualitative Study of the Social Cash Transfer Programme in Urban Zambia
UNICEF study uses Causal Map to explore complex drivers of child work in India
Using AI to facilitate feedback on the learning experiences of doctoral students
Using QuIP and Causal Map in an Evaluation, a WFP interview with DeftEdge
Voscur
What drives group learning, PLI
World Concern. Evaluating a Holistic Community Development Program with QuIP and Causal mapping
World Food Programme, Forcier Consulting
Dual Column
A workflow for collecting and understanding stories at scale – Summary (eval2025)
AI-assisted causal mapping – Summary (validation)
Filters
Path tracing and source tracing
Mapcat Core
A workflow for causal coding with and without AI
Causal mapping for outsiders
Glossary
Minimalist coding for causal mapping
Mapcat Methods
Assessing systems change
Collapsing factor labels and excluding brackets
Combining questions
Comparing groups – What factors or links were mentioned more by some groups than others, in the same map ?
Counting and comparing influences
Different kinds of coding and recoding
Does the evidence support your theory of change ?
Exclude links based on group or other metadata
Focus or exclude factors
Focusing on specific factors. What influences and outcomes are connected to a specific factor ?
Hierarchical coding
Hierarchical coding
Identifying groups – Are there different subgroups within the data ?
Individual questions – introduction
Individual views – How does the system work according to individual sources ?
Looking downstream. What are the direct and indirect consequences of one or more factors ?
Looking upstream. What are the direct and indirect influences on one or more factors ?
Main drivers. Which factors are mentioned most often as drivers ?
Main outcomes. Which factors are mentioned most often as outcomes ?
Names of tables and fields
Opposites
Path tracing – How do one or more causes affect one or more effects, including indirect pathways ?
Path tracing and source tracing
Properties of the causal map – Are there feedback loops ?
Properties of the causal map – Are there leverage points ?
Properties of the causal map – What is the overall structure of the network ?
Properties of the causal map – Which factors are reported as being causally central or causally peripheral ?
Quality assurance at each step of the causal coding workflow
Robustness – How robust is the evidence for that X influences Y ?
Sentiment – Which changes are perceived as most positive or negative ?
Showing group data as custom link labels on the map with optional significance test
Simplification - factor and link frequency
Source tracing – What are the consequences of one or more factors, looking only at stories told in their entirety by individual sources ?
Splitting by groups. Are different groups involved in different ways ?
Summarising – How do the sources claim that the system works, in summary ?
The factors table
Tribes. The most relevantly different subgroups in your data (by causal story)
Vignettes – What is a typical source and what is their story ?
What are the emerging or unexpected factors ?
What are the narratives behind a specific link ?
Which factors and links are mentioned by the most sources ?
Which factors and links were most frequently mentioned ?
Papers and Drafts
A formalisation of causal mapping
A simple measure of the goodness of fit of a causal theory to a text corpus
A workflow for causal coding with and without AI
Animated social map around Donald Trump
Animated social map of US news
Assessing change in (cognitive models of) systems over time
Causal mapping as causal QDA
Causal mapping is an interesting QDA approach which is very suitable for scaling with AI
Causal mapping of loneliness interviews
Combining opposites, sentiment
Despite-claims
Lonely in London
Lonely in London response
Magnetisation
Minimalist coding for causal mapping
Our paper on an inductive workflow to gather and analyse evidence at scale.
Quality assurance at each step of the causal coding workflow
Transforms Filters
Collapsing factor labels and excluding brackets
{'Date': '27/02/2025'}
Case study – Qualia asks about USA problems, again
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🌻 Link metadata – quality of evidence
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24 Oct 2025
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