🗺️ What is Causal Mapping?#

Causal mapping is a technique to visualise what people believe causes what within a complex system. It creates a "mental map" of the cause-and-effect relationships perceived by an individual or a group.

The process starts with narratives—such as interview transcripts, reports, or open-ended survey responses. Causal claims within these texts are systematically identified and structured into a network diagram:

• Nodes (Boxes) represent the factors or concepts (e.g., "Better Training").
• Links (Arrows) show the direction of influence between them.

🛠️ The Causal Map App#

The specialised Causal Map app provides a convenient way to do causal mapping. Users can import interviews or reports and "code" them: highlighting causal claims and adding them to the database. Much of this process can optionally be automated using AI, enabling rigorous analysis of larger datasets.

• Transparency: Every link in the map is transparently tied back to the original source quote. This ensures that outputs are verifiable and avoids acting as a "black box," maintaining the rigour essential for qualitative work.
• Querying the Map: The final map is a dynamic model of causal evidence that can be actively explored to answer sophisticated questions, such as tracing all direct and indirect links from a single input to a defined outcome.
• AI as an Assistant: Generative AI is optionally used as a tireless, low-level coding assistant to quickly extract explicit causal claims from text.