When designing programmes to address complex development challenges, it’s easy to fall into the trap of focusing on surface-level problems and short-term fixes. Traditional tools like the Problem Tree Analysis, often used in the Theory of Change (ToC) process, do a good job of mapping cause-and-effect relationships, but they tend to focus on immediate causes and direct solutions. While this is helpful, it doesn’t always uncover the deeper, systemic, and cultural barriers perpetuating the challenge over time. This is where Causal Layered Analysis (CLA) comes in. 

One key advantage of CLA is that it helps surface transformative solutions. Instead of simply reacting to the current state of the world, CLA invites teams to question existing narratives, reframe problems, and envision alternative futures. This makes it particularly well-suited for UNDP’s work, where innovation and adaptation are essential to achieving sustainable development goals in an uncertain, rapidly changing world.

For example, while a Problem Tree Analysis might identify a lack of job opportunities as a cause of youth unemployment, CLA would take this further by asking ‘What institutional systems contribute to this lack of opportunities?’, ‘What societal beliefs about education or work might limit young people’s choices?’, ‘ What cultural narratives reinforce ideas around success and employment?’.

By answering these questions, teams can design programmes that address systemic barriers, change perceptions, and create opportunities that are better aligned with future needs and realities.