Causal Map is a web‑based platform that lets evaluators, researchers, NGOs and academics code causal statements from interview, survey and report texts to build interactive visual maps of cause‑effect relationships. The tool offers manual and AI‑assisted coding, filtering, group comparison and metrics to identify drivers and outcomes, while linking every claim back to its source for transparent reporting. It also provides consultancy services that handle data coding and deliver customized visual reports to test and refine theories of change.
Funding
Funding not disclosed
Founders
Product
Problem
Evaluators, researchers, NGOs, and academic teams often struggle to turn qualitative interview, survey, and report data into clear evidence about what causes what, making it difficult to test theories of change, compare stakeholder perspectives, and present findings with traceable source links.
Solution
Causal Map provides a web‑based platform that enables users to code causal statements directly from text sources, building interactive visual maps of cause‑effect relationships. The tool supports manual coding and optional AI assistance for large datasets, allowing users to filter, compare groups, and calculate metrics such as “outcomeness” to identify drivers and outcomes. Generated maps link every claim back to its original source, ensuring transparency and auditability. Users can export visual reports, create dashboards, and integrate findings into evaluation workflows or academic research. The platform also offers consultancy services that handle data coding and analysis end‑to‑end, delivering customized visual reports that test and refine theories of change.
Target Audience
Primary users are program evaluators, social‑science researchers, NGOs, foundations, and university departments that need to analyse qualitative data to understand causal mechanisms and test theories of change.
Features
- Manual and AI‑assisted coding of causal links from interview transcripts, surveys, reports, or any text source
- Interactive graph visualisation using Graphviz layout, with drivers shown on the left and outcomes on the right
- Analytical tools for frequency counts, group comparisons, path tracing, feedback‑loop detection, and “outcomeness” scoring
- Exportable visual reports that retain source citations for each causal claim
- Built‑in versioning, bookmarks, and statistics panels for deeper data exploration
- Collaboration features including team sharing, live editing, and access controls (private, pro, team tiers)
- API‑free web interface with optional AI credit add‑ons for advanced coding and answer generation