MKT.10:4.3 - Construct the causal contrast and its credible comparison

When contribution means a causal effect, define the intervention and comparison as ways of acting, including intensity, timing, support and consequential variants. Specify the target population, outcome and horizon. An offer of help and actual use of that help are different interventions. An invitation sent to one team may also affect another team through shared staff or shared information.

Use C.28.CM, Construct and Challenge a Causal Model to explain why the contrast could change the outcome and what else could produce the observations. Recover common causes, selection, intermediate results and feedback that change the inference. A journey diagram or chronological sequence is not a causal model merely because arrows connect its events.

Then obtain an identification answer through MMP.15, Identify an Intervention Effect from Available Data when this requires specialist analysis. The request names the contrast and available data, including how participants entered them. The supplier returns which effect the data can identify, under which assumptions, or a useful bound or reason it remains unidentified. More accurate fitting cannot repair an effect the data do not identify.

Select a feasible comparison suited to the question. Random allocation can make groups comparable for the effect of the assigned intervention if allocation, follow-up and interference conditions are adequate. It does not automatically identify the effect of actual use among self-selected users. Observational comparisons require defensible assumptions about relevant differences, usable variation and measurement. A before-and-after comparison requires a reason that other changes do not explain the result. Asking for “a control group” without these conditions supplies no causal answer.

Design the comparison with its operators. Avoid allocating a promise the service cannot fulfil or depriving people of an existing commitment. Arrange follow-up, exception handling and a stopping decision if the work becomes infeasible. If the credible comparison is unavailable, return the supported descriptive result and the unresolved causal question. A bounded action under uncertainty can still be chosen explicitly.