A persistent challenge in qualitative research is synthesis. When you are working with large volumes of text (interviews, field reports, narrative accounts) across multiple countries and time periods, how do you move systematically from individual accounts to broader patterns? And how do you do so without flattening the complexity that makes qualitative data valuable in the first place?

This piece describes how causal mapping, combined with an AI-assisted coding workflow, helped address that challenge in a large, multi-country research and evaluation project and what researchers working with similarly complex qualitative datasets might take from the experience.

Throughout history humanity has had the urge to predict the future. The Greeks consulted the Delphi Oracle, whereas the Romans inspected sheep entrails and modern day sages poke around tea leaves to get the skinny on the future. This desire to predict the future has found its way into finance where modern day Haruspices pop up on television to make confident boasts about the future direction of the share du jour. All, but the very fortunate of these modern day prophets fail at their impossible task.