🗺️ 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.

🧭 Why Use It and Who is it For?#

Causal mapping is a powerful tool for analysing qualitative data at scale, helping to understand complex, real-world situations.

Who Uses Causal Mapping?#

This technique is primarily used by professionals who need to understand complex social systems and justify their decisions:

• Evaluators: To empirically verify whether a planned programme works as intended (Theory of Change) and trace its actual influence pathways.
• Policymakers & Strategists: To gain a clearer picture of stakeholder perceptions, anticipate risks, and identify effective intervention points (leverage points).
• Researchers: To systematically process large volumes of interview data, often across different groups (e.g., comparing views by location), while keeping data transparent.

Core Utility#

The key benefit is turning massive amounts of qualitative input into a structured visual database which is query-able: you can ask it questions.

• Understand Stakeholder Views: It reveals how different people believe a system or problem works.
• Manage Complexity: It structures messy, interconnected information into a query-able map.
• Validate Arguments: It allows quantifying the robustness of evidence for any causal path reported by stakeholders.

🛠️ 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.