Every programme is built on assumptions. Training will change behaviour. Communities will adopt new practices. Government services will be accessible. Markets will respond to improved supply. These beliefs sit behind every arrow in your theory of change, yet most programmes never test them. This is one of the most common theory of change pitfalls.
When programmes fail, the cause is rarely poor implementation. More often, it is untested assumptions that turned out to be wrong. Isabel Vogel's review of theory of change practice for DFID, drawing on interviews across 25 development organisations, concluded that the quality of a theory of change process rests above all on "making assumptions explicit".
Why Assumptions Matter More Than Activities
Activities are within your control. You can conduct trainings, distribute materials, build infrastructure. But whether those activities produce the intended changes depends entirely on assumptions—conditions outside your direct control that must hold for your logic to work.
A maternal health programme assumes that trained birth attendants will be available when needed. A microfinance programme assumes that borrowers have viable investment opportunities. A girls' education programme assumes that families will allow daughters to attend school. Each assumption is a potential point of failure.
Types of Assumptions
Sue Funnell and Patricia Rogers, in Purposeful Program Theory, argue that a useful theory must spell out the conditions under which each causal link is expected to be necessary and sufficient. It helps to sort those conditions into a few distinct types.
Causal assumptions concern the mechanisms of change. "If people know about nutrition, they will eat better." This assumes knowledge translates to behaviour—an assumption repeatedly contradicted by evidence. A 2024 study of pregnant women, lactating mothers, and young mothers in India found that positive nutrition knowledge and attitudes did not translate into appropriate dietary practices for any micronutrient except iodine.
Contextual assumptions concern the environment. "Government health facilities will be functioning." In many parts of South Asia, this assumption fails more often than it holds.
Implementation assumptions concern delivery capacity. "Field staff will follow the training protocol." Staff turnover, competing priorities, and local adaptation mean implementation rarely matches the design.
Assumption Identification Checklist
- For each arrow in your ToC, ask: "Under what conditions does this connection hold?"
- For each actor, ask: "What must they do, and why would they do it?"
- For each context, ask: "What must remain stable for this to work?"
- For each resource, ask: "What must be available, and who controls access?"
How to Test Assumptions
Formative research before programme launch can test critical assumptions cheaply. As BetterEvaluation notes in summarising Vogel's review, a reasoned theory of change should rest on assumptions that are plausible, supported by evidence, and agreed by key players—conditions you can probe before you build. Focus group discussions, key informant interviews, and market assessments reveal whether your assumptions match reality. Ensuring data quality in the field is essential for these assessments to be trustworthy. A few weeks of formative research can save years of misdirected effort.
Pilot studies test assumptions under real conditions at small scale. The key is testing not just whether the intervention works, but specifically which assumptions hold and which fail. Combining qualitative and quantitative approaches through mixed methods can strengthen this testing.
Rapid assessments during implementation can check assumptions as contexts change. Seasonal patterns, political shifts, and economic changes can invalidate assumptions that were sound at the design stage.
The pre-mortem tests assumptions before a single rupee is spent. Developed by psychologist Gary Klein and described in Harvard Business Review, the technique asks the team to imagine the programme has already failed a year from now, then list the reasons why. By drawing on what Klein calls "prospective hindsight," it surfaces the fragile assumptions that overconfidence and groupthink would otherwise keep unspoken—exactly the ones a design team most needs to name.
"An assumption register is as important as a risk register. In fact, your biggest risks are your untested assumptions."
South Asian Contexts Where Assumptions Break
Caste dynamics affect who accesses services, who participates in groups, and whose voice is heard. Programmes that assume equal access often reproduce existing inequalities. Choosing indicators that matter requires surfacing these dynamics explicitly.
Gender norms shape mobility, decision-making, and resource control in ways that programme designs frequently underestimate. Assuming women can attend trainings, access markets, or control income often fails without specific strategies to address normative barriers.
Seasonal migration means that target populations may be physically absent during key programme periods. Assuming year-round availability of communities is a common failure point.
Building an Assumption Practice
Make assumption identification a standard part of programme design. The discipline is not new: the logical framework tradition that donors such as USAID and DFID have used for decades already requires teams to name the external assumptions that must hold for results to follow—the fourth column of the classic logframe matrix—and to assess each for criticality and risk. Create an assumption register that tracks each critical assumption, the evidence for or against it, and the plan for testing it. Review assumptions quarterly. And have the courage to redesign when assumptions prove wrong—that is adaptive management in action.