We study the functional average treatment effect on the treated in a two-period difference-in-differences design. Under conditional functional parallel trends, we derive the full-curve efficient influence function and efficiency bound and construct a cross-fitted, doubly robust estimator. Under ultra-dense observation, bounds on curve-estimation error and learner stability preserve its full-data first-order behavior. Nuisance-rate and moment conditions yield asymptotic linearity in $L^2$; additional Sobolev regularity gives uniform Gaussian limits and valid continuous-domain bands computed on a refining grid. In simulations, estimation errors decrease with sample size under single-model misspecification, and bands accounting for nuisance estimation have near-nominal or conservative coverage. Further experiments examine basis-truncation bias and nuisance-estimation uncertainty. An application to electricity load profiles from 4389 eligible households estimates a modest reduction concentrated in evening consumption among dynamic-tariff participants.

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