We develop a semiparametric continuous time inferential framework for testing cumulative treatment effects, including both immediate and carryover effects. The latent state process is modeled by an ordinary differential equation (ODE) whose drift depends on state and treatment, while the observed data are noisy measurements of this latent state at irregular, multi resolution time points. We define the average treatment effect (ATE) as the difference of expected cumulative outcomes under the always treat and always control policies and establish its identifiability from observational data. More generally, the framework accommodates contrasts between arbitrary deterministic stationary treatment policies, including clinically motivated threshold policies, with the always treat versus never treat comparison as the leading special case. A two step estimation procedure first recovers the latent state trajectories and their derivatives via spline smoothing, then solves a continuous time estimating equation derived from the infinitesimal generator of the ODE. We prove asymptotic normality of the resulting test statistic under explicit rate conditions on the spline estimator. The resulting test admits asymptotically valid inference under noisy, irregularly sampled observations, providing a studentized statistic with a standard $N(0,1)$ limit. Simulations show that the proposed method demonstrates improved stability and power over discrete time alternatives in noisy and multi resolution settings, with comparable performance under clean regular grids. We apply the method to the OhioT1DM diabetes dataset to evaluate cumulative effects of insulin treatments on glucose levels.

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