MMP.14:4.4 - Change the part that explains the consequential mismatch
First trace the disputed prediction through its calculation and observation meaning. A wrong unit, event label, numerical solution or censoring convention can require correction without changing the underlying subject relation.
Then formulate a small number of plausible revisions. Show the changed mathematical component and why it can affect the discrepancy:
| Located feature | Possible construction to examine |
|---|---|
| Residual means vary with an omitted input | Replace (m_0(x)) by (m_0(x)+b,h(x)), with a subject-admissible function (h); estimate (b) and recalculate the relevant conditional response. |
| Dispersion varies by input while the mean remains adequate | Replace constant error scale by a positive function (s(x)); compare conditional spread and the receiving tail probability. |
| Residual sequences have dependence absent from the model | Replace independent errors by a specified covariance or a recurrence such as (e_t=\rho e_{t-1}+\eta_t); derive the resulting block or horizon prediction. |
| Available records exclude outcomes the prediction includes | Change the recording or selection component using the established inclusion rule; predict the retained records, keeping the latent law separately visible. |
These are candidates, not conclusions from the symptom alone. Do not delete observations or inflate noise until everything passes. Several changes can reproduce the feature; use subject knowledge and existing discriminating observations to choose, retain conditional alternatives, or return the missing distinction.
Use MMP.11 to preserve support, constraints and known relations when extending the family. Use MMP.13 to infer the revised unknowns; MMP.12 supplies a justified restriction when added flexibility makes recovery unstable. Changed priors, constraints or noise laws can change the answer without adding information to the records.
If the remaining difference is worth a new observation, return the distinguishing prediction and feasible-design question to the applicable observation-design and subject methods. A predictive repair does not identify an intervention effect; obtain the required causal assumptions and identification separately when that is the receiving question. A narrower supported use can finish without either continuation.