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：治疗政策定义了在何时施加何种治疗以影响某个感兴趣结局。数据驱动的决策制定需要预测政策改变时会发生什么。现有预测不同场景下结局演变的方法假设未来的治疗序列是预先固定的，而实践中治疗是由政策随机确定的，并且可能依赖于例如先前治疗的疗效。因此，如果治疗政策未知或需要进行反事实分析，当前方法将不再适用。为解决这些局限，我们通过结合高斯过程和点过程，在连续时间上联合建模治疗与结局。我们的模型能够从治疗与结局的观测序列中估计治疗政策，并在对治疗政策进行干预后（区别于单一治疗的因果效应），预测结局的干预性及反事实演变过程。我们通过血糖进程的真实世界与半合成数据证明，该方法能比现有替代方案更准确地回答因果查询问题。