Theory of Change reconstruction, and the fiction that propagates downstream.
Theory of Change reconstruction is one of the standard early tasks in an evaluation. When a programme has been running for years without a coherent results architecture, or has run through multiple design iterations, or has never had its causal logic written down in a form that can be tested, the reconstruction produces the framework the rest of the evaluation works against. Activities. Outputs. Intermediate outcomes. Impact. The assumptions that link each level to the next. The distinction between what the design said and what the programme actually did.
Where the fiction enters, and why it propagates.
The most common failure when AI is used for Theory of Change reconstruction is causal fabrication. The model produces a pathway that reads coherently, links activities to outcomes through plausible mechanisms, and identifies assumptions that fit the logic. None of it is actually anchored in the programme documents. The links are inferred from the model's general knowledge of how programmes of this type typically work, not surfaced from the specific evidence base. When the reconstruction is presented to the programme team, the response is often that it looks right in the abstract but doesn't describe what this programme actually does.
The second failure is over-linearisation. A theory-based evaluation approaches causality as a set of interacting pathways, feedback loops, and context-dependent mechanisms. Large language models trained on standard results-chain notation tend to produce clean linear diagrams even when the underlying programme logic is genuinely non-linear. Complex causality gets collapsed into a sequence of arrows. Feedback and mutual influence disappear. The reconstruction misrepresents the programme by making it look tidier than it is.
Three structural controls, each answering a specific failure mode.
Three provenance classes answer causal fabrication. Every element of the reconstructed Theory of Change carries one of three markings on the artefact itself. Stated: written verbatim in a programme document, quotable with a reference. Synthesised: the evaluator's formulation, drawing on the documents but not found in them. Inferred: derived by reasoning where the documents are silent. Synthesised and inferred elements are never silently promoted to stated; they carry a validation route and a named person to validate with. The deck shows which is which. This is the discipline that makes silent gap-filling structurally impossible rather than only discouraged.
The complexity discipline answers over-linearisation. The reconstruction workflow produces two artefacts, not one. A linear results chain for donor-facing use, which serves the reporting purpose it exists for. And a more complex causal map alongside it, which shows the feedback loops, contextual dependencies, and non-linear mechanisms that a linear chain cannot represent. When a programme's logic is genuinely linear, the two artefacts converge. When it isn't, the difference between them is where the evaluation actually needs to look. Programme teams and evaluators are asked to review both, not the linear chain alone.
A validation deck carrying the chain, the provenance markings, the assumptions register, the version history, and the disclosure.
Every Meridian Theory of Change reconstruction is delivered as a validation deck. The deck carries the results chain, the complexity map, the provenance markings on every element, the assumptions register mapped to each causal link, the version history distinguishing design-as-stated, design-as-evolved, and design-as-currently-understood, and the disclosure text. The deck is a working analytical artefact, designed to be argued over in a validation meeting rather than filed. A reconstruction that comes back unchanged after validation usually means it was not read.
The reconstruction is delivered as a preliminary framework for stakeholder validation, not as a finding. Programme teams review it, refine it, contest it. The validated version is what the evaluation then works against. A Theory of Change reconstruction that arrives as a finding closes conversations that need to stay open.
The differentiation is the discipline.
Theory of Change reconstruction is where AI failure compounds hardest, because every downstream method then works honestly against a fictional object. The provenance classes make silent gap-filling structurally impossible. The validation deck goes to a meeting to be argued over, not to file. A reconstruction that comes back unchanged after validation usually means it was not read.