A treatment policy defines when and what treatments are applied to affect some outcome of interest. Data-driven decision-making requires the ability to predict what happens if a policy is changed. Existing methods that predict how the outcome evolves under different scenarios assume that the tentative sequences of future treatments are fixed in advance, while in practice the treatments are determined stochastically by a policy and may depend, for example, on the efficiency of previous treatments. Therefore, the current methods are not applicable if the treatment policy is unknown or a counterfactual analysis is needed. To handle these limitations, we model the treatments and outcomes jointly in continuous time, by combining Gaussian processes and point processes. Our model enables the estimation of a treatment policy from observational sequences of treatments and outcomes, and it can predict the interventional and counterfactual progression of the outcome after an intervention on the treatment policy (in contrast with the causal effect of a single treatment). We show with real-world and semi-synthetic data on blood glucose progression that our method can answer causal queries more accurately than existing alternatives.

翻译：治疗策略定义了何时以及如何应用治疗以影响某个感兴趣的结果。数据驱动的决策需要预测策略改变时会发生什么。现有方法在预测不同情景下的结果演变时，假设未来的治疗序列是预先确定的，而实践中治疗是由策略随机决定的，可能依赖于先前治疗的效率等因素。因此，当治疗策略未知或需要进行反事实分析时，当前方法不再适用。为克服这些局限性，我们通过结合高斯过程和点过程，对治疗和结果进行连续时间联合建模。该模型能够从观测到的治疗序列和结果序列中估计治疗策略，并预测在治疗策略干预（而非单次治疗的因果效应）后结果的干预性和反事实演变过程。我们使用血糖进展的真实世界和半合成数据表明，该方法在回答因果查询方面比现有替代方法更准确。