Theory of Change
Theory of Change (ToC) is a methodology for planning, monitoring, and evaluating interventions. It describes the causal pathway from activities to long-term impact, helping you articulate how and why your program will achieve its intended outcomes.
What is a Logic Model?
A logic model is the visual representation of a Theory of Change. In MUSE, logic models consist of five stages connected by causal arrows:
The Five Stages
| Stage | Color | Description | Example |
|---|---|---|---|
| Activities | Orange | Concrete actions your program takes | Conducting workshops, distributing materials |
| Outputs | Green | Direct products or deliverables of activities | Number of workshops held, materials distributed |
| Short-term Outcomes | Blue | Immediate changes in knowledge, attitudes, or behavior | Increased awareness, skill development |
| Intermediate Outcomes | Yellow | Medium-term changes that result from short-term outcomes | Policy changes, community adoption |
| Impact | Purple | Long-term societal change — the ultimate goal | Reduced poverty, improved public health |
How They Connect
Activities → Outputs → Short-term Outcomes → Intermediate Outcomes → Impact
Each arrow represents a causal relationship — the claim that one stage leads to the next. In MUSE, these causal connections can be backed by peer-reviewed research evidence, making your Theory of Change more credible and defensible.
Why Use Theory of Change?
- Clarity — Forces you to articulate your assumptions about how change happens
- Evidence-based planning — Connects your program design to research findings
- Accountability — Creates a clear framework for measuring progress
- Communication — Helps stakeholders understand your program's logic
- Evaluation — Provides a roadmap for what to measure and when
Theory of Change in MUSE
MUSE brings Theory of Change into the digital age:
- Visual Canvas — Build your logic model on an interactive drag-and-drop canvas
- AI Generation — Describe your intervention and let AI generate a complete logic model
- Evidence Linking — AI automatically searches and links relevant research to each causal connection
- Metrics — Attach measurable indicators to any stage of your model
- Blockchain Verification — Mint your logic model as a Hypercert for transparent, verifiable impact claims
- Sharing — Save to IPFS and share your logic model with anyone via a permanent link
Example: Education Program
Imagine you're planning a program to improve literacy in rural communities:
| Stage | Example |
|---|---|
| Activities | Distribute tablets with educational apps to schools |
| Outputs | 500 tablets distributed to 20 schools |
| Short-term Outcomes | Students use tablets 3+ hours per week; reading scores improve 15% |
| Intermediate Outcomes | Schools adopt digital curriculum; teacher capacity increases |
| Impact | Improved literacy rates across the region |
Each connection between stages can be supported by research evidence. For example, the link between "tablet distribution" and "improved reading scores" might be backed by randomized controlled trials showing the effectiveness of educational technology.
Common Misconceptions
"My program is too simple for a Theory of Change." Even simple programs benefit from mapping their logic. The process of building a ToC often surfaces hidden assumptions and gaps in program design — before they become real-world problems.
"I need to have all the evidence before I start." You don't. MUSE's AI can help identify where evidence exists and where your program relies on reasonable assumptions. Knowing your evidence gaps is just as valuable as knowing your strengths.
"Theory of Change is only for evaluation teams." Theory of Change is most powerful when it is built collaboratively with program designers, implementers, funders, and community members. Everyone benefits from a shared understanding of how change is expected to happen.