# Case studies
Posts
A workflow for collecting and understanding stories at scale, supported by artificial intelligence
Paper in Evaluation journal
Pilot Universal Child Benefit Programme in Kenya | UNICEF Kenya
In response to the COVID-19 pandemic, the Government of the Republic of Kenya piloted a Universal Child Benefit (UCB) Programme with support from UNICEF from December 2021 to December 2022, targeting children aged 0–36 months in Kajiado, Embu and Kisumu Counties. The UCB pilot provided KES 800 per month per child, distributed bi-monthly via M-Pesa to female caregivers, and included complementary services to address malnutrition, negative parenting practices and disability exclusion. A qualitative study assessed the pilot’s implementation, accessibility, impact and sustainability, using interviews and focus groups to inform future policy development and scale-up opportunities. The study uses QuIP methodology and Causal Map. Download.
Tree Aid: Empowering Communities Through Forest Management in Burkina Faso
Causal Map partnered with Tree Aid to evaluate their Local Governance of Forest Resources (WEOOG PAANI) project in Burkina Faso. Using innovative methods, we assessed the project's impact on forest governance, household and food consumption amongst project beneficiaries in two communes.
Using QuIP and Causal Map in an Evaluation, a WFP interview with DeftEdge
Alexandra Priebe from the World Food Programme, interviewing Ashley Hollister and Sarang Mangi from the DeftEdge on their experience on using QuIP and Causal Map in their evaluation.
Our seamless stories workflow in practice
An evaluation project about gender gaps faced by women pursuing STEM careers at DuocUC.
Thinking together within and beyond Communities of Practice
Papers/publications in which Causal Map was featured or mentioned
An M&E time machine
A proof of concept study about using AI to collect and analyse data in complex systems.
