What is causal mapping?

People who run programmes, whether in international development, public health, education, or social policy, need to know whether those programmes work, and why. Researchers and consultants studying complex problems need to understand what people say causes what. That is where causal mapping comes in.

Who uses it, and for what?

Causal Map is used by four main groups, often overlapping:

Evaluators need to check whether a programme produced the changes it was supposed to. Funders and programme managers set out a “theory of change”: if we do X, it should lead to Y, which should lead to Z. Causal mapping lets you test that theory against what participants, staff, and beneficiaries actually say happened.

Researchers in social science, public health, and development want to structure qualitative findings around causal claims, not just themes. Causal mapping captures what people say leads to what, so you can compare accounts, trace pathways, and identify what matters most.

NGOs and foundations learn from their programmes and report to donors. A causal map shows, visually, what the evidence says about how change happened (or did not), with every finding traceable to the source. See case studies for examples.

University departments use causal mapping in teaching and in hands-on student research. Students can work directly with interview data and produce rigorous, visual findings.

What does “coding” mean here?

In qualitative research, “coding” means reading through your sources (interviews, reports, survey responses) and systematically marking what you find. In causal mapping, you mark the causal claims people make: when someone says “the training gave me more confidence to speak up”, you highlight that as a causal link from “training” to “confidence to speak up”. The Causal Map app builds a visual map from these links as you go.

What can you do with the results?

Once you have coded your data, you can ask questions that ordinary thematic analysis cannot reach. For example: which factors do people mention most as drivers or outcomes? Do different groups (men and women, staff and beneficiaries, different regions) describe different pathways? Does the evidence support your theory of change, and where does it break down? What is unexpected? Are there feedback loops or leverage points in the system?

The Causal Mapping Guide documents over twenty specific question types with worked examples, from frequency counts and group comparisons to path tracing and systems analysis.

How it differs from thematic analysis

Thematic analysis identifies recurring topics. Causal mapping identifies the relationships between those topics: which factors people say influence which outcomes, and how. A thematic analysis might tell you that participants mention “training” and “confidence”. A causal map shows that participants say training leads to confidence, which groups say this more than others, and what other factors feed into or follow from that connection.

What you need to get started

Any text where people describe causes and effects: interview transcripts, focus group notes, open-ended survey responses, project reports. A research question that involves understanding why things happen, not just what happens. That is all.

The Causal Map app

Causal Map is the only software built specifically for causal mapping. It handles the full workflow: upload sources, code causal links (by hand or with optional AI assistance for larger datasets), filter and analyse, generate visual reports. It works for small student projects and for large evaluations with hundreds of documents.

Read the full Causal Mapping Guide for detailed methods, worked examples, and best practice. Have questions? Get in touch.