In our previous blog, we explored why Test and Learn is gaining momentum across government and why learning during delivery is increasingly important when tackling complex challenges. But this raises a practical question: if programmes operate in uncertain environments, how do we decide what is most important to learn?
Test and Learn begins by identifying the assumptions that matter most. A theory of change provides a practical framework for making those assumptions explicit and translating them into a focused learning agenda.
Why a theory of change is valuable for Test and Learn
At the heart of any Test and Learn approach is a simple question: what do we need to know?
Test and Learn recognises that programmes do not begin with perfect solutions. In complex and fast-changing environments, delivery contexts evolve, policy priorities shift, and new evidence emerges throughout implementation. As a result, programmes start with informed assumptions about how change is expected to happen.
This is where a theory of change becomes valuable.
While theories of change are often viewed as planning, accountability, or communication tools, their greatest value for Test and Learn lies in providing a structured way to make assumptions visible. They set out how activities are expected to contribute to outcomes and, crucially, the conditions that need to hold for change to occur.
This matters because Test and Learn is fundamentally concerned with uncertainty. If organisations do not make their assumptions explicit, it becomes difficult to understand what should be tested, what evidence is needed, and how delivery should adapt when circumstances change.
Rather than treating programme design as a fixed blueprint, a theory of change encourages organisations to think in terms of hypotheses. Assumptions about behaviour, incentives, institutions, partnerships, and context become propositions that can be explored, tested, and refined over time.
For this reason, a theory of change provides a natural starting point for any Test & Learn approach.
How a theory of change identifies learning priorities
In our experience, one of the most valuable aspects of developing a theory of change is not the final product itself, but the process of identifying, prioritising, and revisiting the assumptions that matter most.
The development process often reveals that different stakeholders hold different understandings of how change is expected to happen. Assumptions that were previously implicit become visible. Areas of uncertainty become clearer. Stakeholders begin to develop a shared understanding of where the greatest risks and uncertainties lie.
This creates a practical foundation for learning.
Most programmes contain dozens of assumptions at varying levels of maturity, understanding, and importance. The task is not to test everything. It is to identify which assumptions are most important for understanding whether a programme can achieve its intended objectives.
Rather than asking broad questions such as ‘is the programme working?’, organisations can use their theory of change to develop more targeted learning questions:
- Which assumptions are most critical to achieving outcomes?
- Which assumptions represent the greatest risk or uncertainty?
- What evidence would help us understand whether those assumptions hold in practice?
The result is a more focused approach to learning, with evidence generation directed towards the questions that matter most for decision-making and adaptive delivery embedded from the outset.
Putting theory into practice
At Itad, we have applied this approach across a range of complex government-funded programmes.
For the UK Government’s Blue Planet Fund, we worked with Defra and Foreign, Commonwealth and Development Office stakeholders to develop a portfolio-level theory of change that helped identify where key assumptions were uncertain and where learning would be most valuable.
For example, the portfolio theory of change highlighted a core area of uncertainty: how and to what extent the individual programmes within the portfolio were contributing to shared objectives. This became a priority learning question that informed targeted evidence generation and helped Defra refine its governance and accountability arrangements to focus on shared outcomes rather than operational compliance.
The portfolio theory of change has also provided the foundation for several theory-based evaluative activities, helping test critical assumptions within the portfolio. More recently, Defra has used it to support its portfolio review and rationalisation process, using the theory of change as a framework for aligning programmes around shared objectives and strengthening strategic coherence.
A similar approach has been used within the Climate and Ocean Adaptation and Sustainable Transition (COAST) programme. Through a participatory theory of change process, we worked with stakeholders to identify the assumptions carrying the greatest uncertainty and where learning was most urgently needed.
As delivery has progressed, learning priorities have evolved. Early learning focused on understanding effective partnerships. Attention then shifted to questions around scaling innovation and, more recently, to clarifying COAST’s role within the wider blue finance landscape. Throughout this process, the theory of change has provided a consistent framework for identifying what matters most at each stage of implementation and ensuring evidence generation remains relevant to programme decision-making.
COAST’s recent process evaluation found that programme learning activities provide timely and relevant insights that are actively used to adjust strategies, activities and resource allocation in real time. Combined with a flexible approach to adapting priorities, workplans and budgets, this has enabled programme partners to evolve their activities as they learn what is and is not working.
In a Test and Learn approach, the theory of change functions as a living framework that evolves as assumptions are tested and learning emerges.
Defra’s Ocean Community Empowerment and Nature (OCEAN) programme provides an example of this in practice. During inception, the theory of change was reviewed collaboratively by Defra, the evaluation team, and the grant administrator to develop a simplified and evaluable pathway with explicit assumptions to test.
As evidence emerged through the interim evaluation, programme delivery was adapted and these changes will be reflected in future iterations of the theory of change. This creates a continuous cycle of testing, learning and refinement, ensuring that both delivery and programme strategy evolve in response to evidence. It also creates opportunities for collective reflection on what has been learned, which assumptions remain uncertain, and where future learning should be focused.
The takeaway
In a Test and Learn approach, a theory of change creates a structured way to identify what matters most, make uncertainty visible and focus learning where it can have the greatest influence on decisions.
By making assumptions explicit, helping stakeholders identify where uncertainty matters most, and creating a structured basis for prioritising learning questions, it enables organisations to focus evidence generation on the issues that are most important for delivery and decision-making. When treated as a living framework that evolves alongside evidence and experience, it helps organisations move beyond reporting what happened towards actively improving what happens next.
From our experience, this approach works best when organisations:
- Make critical assumptions explicit.
- Prioritise learning where uncertainty matters most.
- Focus evidence generation and learning agendas around decision-making needs.
- Keep the theory of change under review as learning emerges.
When used in this way a theory of change becomes more than a description of how change is expected to happen. It becomes a valuable tool for testing assumptions, generating actionable evidence and supporting adaptive delivery in complex and uncertain environments.
To learn more about how Test and Learn approaches can support adaptive delivery and evidence-informed decision-making, contact us or explore the other articles in our Test and Learn series.
