What if artificial intelligence could unravel the hidden connections behind the success - or failure - of complex programs?
On October 10-11, 2024, the South African Monitoring and Evaluation Association (SAMEA) Conference took place in Johannesburg, bringing together professionals ready to rethink how we measure change. Among the standout sessions was a workshop facilitated by Dena Lomofsky, Partner and Senior Consultant at Southern Hemisphere, where she showcased how Causal Map AI was used to uncover meaningful insights and strengthen program evaluation with deeper insights and clearer impact pathways.
During the workshop, Dena presented how we at Causal Map collaborated with Southern Hemisphere to use Causal Map AI in a mid-term review of the Love Alliance project. This project spans multiple African countries and focuses on health, capacity building, partnerships, and advocacy.
By analysing large volumes of textual data from interviews and documents, we were able to identify key drivers and outcomes. Using Causal Map AI allowed us to create comprehensive causal maps that served as evidence-based Theories of Change (ToC). This visual representation helped Southern Hemisphere to compare AI-generated maps with the official project ToC, providing insights into the project's impact pathways and supporting their Contribution Analysis approach.
Here's a snapshot of the presentation:
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How causal map helped us?
Causal mapping helped us make sense of narrative data by
capturing sections where sources say that one thing caused or influenced another
Aggregate and compare causal pathways across different
countries & stakeholder groups in a transparent and rigorous way
Used AI to code large amounts of text, but still rigorously
Answered evaluation questions with causal maps and tables
Supported contribution analysis as an evaluation approach
Provided a quantitative element to the causal relationships
Visualize complex causal pathways from program activities to outcomes
Working alongside Dena and her team was a rewarding experience. We learned a great deal about effectively integrating AI into evaluation methodologies, particularly through iterative conversations that refined our approach.
This experience has reinforced our belief in the transformative potential of AI-driven evaluation. As we continue to explore innovative approaches like AI-assisted causal mapping, we are excited about the future possibilities for enhancing evidence-based decision-making in complex contexts.
Between September 23th to September 27th, Steve Powell and Fiona Remnant were in Rimini, Italy, to present at the European Evaluation Society Conference. Causal Map participated in many different activities, from facilitating a workshop to doing a demo live. Check out the highlights.
