Contribution analysis is about testing and refining a theory of change to build a credible case for contribution. It's focused on a specific theory of change and on the contributions to outcomes. These contributions might happen along a causal chain, but CA tends to be less explicit about how they are to be traced.

Outcome harvesting is about collecting and explaining a (hopefully long and substantial) list of intended and unintended outcomes after the fact, and identifying how the programme contributed to the outcomes. But it's just like a list of cause-effect relationships. It can be a challenge to understand how those causes and effects overlap with one another or influence one another.

Causal mapping has some limitations. First, the credibility of the causal arguments which can be derived from a map is limited by the credibility of the original data sources. We see the job of causal mapping as collecting, organising and synthesising a large number of claims about what causes what; drawing conclusions about what this actually reveals about the world is a final step that goes beyond causal mapping per se. In specific cases, establishing explicit and context-specific rules of inference may help to make this final step.

Responding to our one-page description of causal mapping, Julian King says the elephant in the room with causal mapping is: can causal mapping really help you get from causal opinions to causal inference?

Weird image of people counting beans generated by Canva's AI

Causal maps help us to assemble evidence for the causal processes at work in specified domains, including the influence of activities being evaluated. They can also help expose differences between the evidence given by different sources and differences between the analysed data and theories of change derived from other sources, including those officially espoused by the commissioner of the evaluation (Powell et al., 2023). The identification of differences in understanding can then feed into further enquiry, analysis and action concerning why people have different views, what the implications of this are and how these might be addressed.

To evaluate a program, the evaluator can use Contribution Analysis (CA) [@mayneMakingCausalClaims2012]. We start with a program logic or Theory of Change (ToC), consisting of possible pathways from interventions to outcomes, and collect existing or new evidence for each link. However evaluators can often not assume that the ToC underpinning a program aligns with the realities on the ground, or they may uncover outcomes not anticipated in the original program design - see Koleros & Mayne (2019). We have argued (Powell, Copestake, et al., 2023, p. 114) for a generalisation of CA in which evidence relevant to constructing a program theory, as well as evidence for the causal influences flowing through it, are both collected at the same time, without the evaluator (necessarily) having a prior theory. In this sense, following Mayne, “program theory” need not be something that any person necessarily possessed or articulated at the time, but is something which can be approximated and improved during the evaluation process.

What is the output of an evaluation? We have a report, hopefully answering the evaluation questions. (In the sense of developmental evaluation perhaps some learning has taken place as well, or as a main output, but this is not what I want to address here.)