Causal Loop Diagrams: Ways to Use


A CLD is a powerful systems thinking tool to characterize operation of a complex system or a problem. A CLD visually maps key variables and their causal relationships. Variables may include:

  • factors
  • issues
  • processes
  • actors’ behaviors and perceptions

Additionally, a CLD can be used to uncover underlying structures and associated feedback loops that produce recurring patterns of events over time.


At a high level, CLDs help us do the following, which can form the foundation for many program design, monitoring, and evaluation activities:

  • Defining the problem: Effective program design efforts must begin with a robust understanding of the local context/system and the problem to be addressed. CLDs provide an integrated view into all key variables (i.e., all relevant stakeholders and their perspectives, STEEP factors, and processes) and their causal relationships in a local system. (STEEP stands for social technological, economic, environmental and political factors.) These causal relationships form feedback loops through which we can trace the chain of events/influences that condition and inform behavior patterns and outcomes in a local system. By examining a CLD, we can identify root causes of problems and causal pathways that sustain the problem. Additionally, a CLD captures stakeholder interests, perspectives and concerns as they relate to the operation of various factors and processes within a local context. As such, they help us uncover incentives and sanction structures built into the local system that motivate certain behaviors. This can help program designers understand which stakeholders need to be incorporated into collaboration efforts and how they can be incentivized for cooperation. These insights strengthen a program’s theory of change and the odds of success for its engagement and intervention activities.
  • Identifying intervention points. Because a CLD maps causal interactions and interdependencies that explain how problems emerge and are sustained in a particular environment, it can also provide insights into how we can initiate change for system-wide improvement. Through leverage analysis, a CLD can be assessed to identify actionable points within a system (e.g., hub points, parameters, buffers, information flows, rules, power structures, governance, roles, etc.) and high and low-leverage intervention points can be compared for trade-offs in various effects.
  • Informing monitoring and evaluation efforts. CLDs can also inform the measurement scheme supporting system-wide monitoring and evaluation efforts. Key variables in causal pathways associated with outcomes of interest provide helpful input into the design of monitoring and evaluation frameworks, helping identify what critical factors to track and measure during the course of program implementation and evaluation.
  • Enhancing stakeholder participation and input. CLDs are shown to be most effective when developed through a participatory modeling process that brings together diverse stakeholders to share information and ideas about their system. CLDs help externalize stakeholders’ mental models while helping them develop a shared understanding of the problem and a sense of ownership of the resulting program efforts.


Key Applications

  • Characterize complex causal relationships between key variables
  • Uncover feedback structures and root causes that drive systemic outcomes
  • Identify system parts/variables separated by time and space
  • Consider the entire system together and recognize outcomes are a result of the entire system working together

Potential Limitations

  • Represent simplification of the reality
  • Based on modelers’ subjective perspectives
  • Reveal qualitative (not quantitative) insights
  • Cannot conclusively predict outcomes!

Click here to read Case Studies of CLD in action.