Digital twins are increasingly used to ask how a patient's health would evolve under alternative treatment strategies. When a twin couples a mechanistic model with learned components and is fitted to observational records, its answer mixes information from the data with assumptions from the mechanism. For sustained strategies that begin at a clinical landmark, we split the counterfactual mean into a part the data support, where patients follow the strategy with probability bounded away from zero, and a part they do not. On the supported part, a cross-fitted sequential doubly robust estimator that uses the twin as its outcome model is consistent whenever the decision model is, so twins that fit equally well but disagree as plug-in estimators agree after correction. On the unsupported part, we characterize the values compatible with the data and with a residual of size at most epsilon around the mechanism. This set is nested in epsilon, reduces to a point when the mechanism is exact and identifiable, and recovers assumption-free bounds as epsilon grows, so the penalty weight of a physics-informed loss becomes a sensitivity parameter. Knowledge constraints on how covariates enter the mechanism shrink the set, and when support grows over time, back-testing gives a data-based lower bound on epsilon. In simulations based on an energy-balance model of weight change under glucagon-like peptide-1 receptor agonist therapy, the correction reduced the spread of estimates across four twins from 0.66 to 0.03 percentage points of weight loss with conservative coverage; on unsupported patients, the exact mechanism was rejected in every replicate, the bounds covered the truth at every residual size not rejected, and a mechanism-free extrapolation was biased by 0.5 points.

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