Finding optimal treatment plans in continuous time is crucial for personalized medicine. Existing methods, however, do not explicitly optimize treatment plans and instead focus solely on predicting treatment effects. Naive optimization over these treatment-response models can exploit model errors, resulting in plans that appear optimal under the learned model but perform poorly in practice. To mitigate model exploitation, we propose SigCTO (Signature-based Conservative Treatment Optimization), a conservative framework for continuous-time treatment optimization. We derive a path-space regularizer based on signature-kernel maximum mean discrepancy that penalizes treatment plans for which predicted and true responses differ. Under suitable assumptions, the regularizer allows for consistent estimation, and the regularized objective upper bounds the true treatment cost. Experiments on pharmacometric simulators show that SigCTO finds treatment plans with lower ground-truth costs than state-of-the-art baselines. On real-world ICU data, SigCTO predicts treatment cost more accurately than baselines, and the regularizer detects prediction errors.

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