Adaptive enrichment trials that identify responsive subpopulations allocate a fixed sample across them and estimate each one's effect from the resulting data. For a synthetic control design with adaptive recruitment and borrowing of control outcomes, we ask whether a doubly robust family of targeted maximum likelihood estimators should replace its sample mean; the choice can be made largely at the design stage. A crossing condition weighs the bias removed and the efficiency gained by the doubly robust family against the finite-sample inflation of nuisance estimation; an equal-error boundary, located by simulation, separates the families in the plane of sample size and bias. A consistency principle prices recruiting with the wrong family's variance functional. With three and eight simulated subpopulations the boundary's sign prediction for the mean squared error difference holds in $99.6\%$ of $10{,}302$ comparisons decided at two standard errors. A pre-specified selector commits to the doubly robust family where the squared anticipated bias exceeds the net variance term. Recruiting along that family's path costs up to $0.035$ in the area under the receiver operating characteristic curve; recruiting with the sample mean functional and routing only the analysis removes this cost and about nine tenths of its error excess.

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