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。数据驱动的决策制定需要具备预测策略改变后会发生什么的能力。现有方法能够预测不同场景下结果的演变，但这些方法假设未来的治疗序列是预先确定的，而实际上治疗是由策略随机决定的，并且可能依赖于先前治疗的效果等因素。因此，如果治疗策略未知或需要进行反事实分析，当前方法将不再适用。为了解决这些局限性，我们通过结合高斯过程和点过程，在连续时间内对治疗和结果进行联合建模。我们的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，并且可以预测在对治疗策略进行干预后（与单一治疗的因果效应相对比）结果在干预和反事实条件下的演变过程。我们使用关于血糖进展的真实世界数据和半合成数据证明，我们的方法能够比现有替代方法更准确地回答因果查询问题。