Machine learning models based on temporal point processes are the state of the art in a wide variety of applications involving discrete events in continuous time. However, these models lack the ability to answer counterfactual questions, which are increasingly relevant as these models are being used to inform targeted interventions. In this work, our goal is to fill this gap. To this end, we first develop a causal model of thinning for temporal point processes that builds upon the Gumbel-Max structural causal model. This model satisfies a desirable counterfactual monotonicity condition, which is sufficient to identify counterfactual dynamics in the process of thinning. Then, given an observed realization of a temporal point process with a given intensity function, we develop a sampling algorithm that uses the above causal model of thinning and the superposition theorem to simulate counterfactual realizations of the temporal point process under a given alternative intensity function. Simulation experiments using synthetic and real epidemiological data show that the counterfactual realizations provided by our algorithm may give valuable insights to enhance targeted interventions.
翻译:以时间点过程为基础的机器学习模型是一系列不同应用中涉及连续时间的离散事件的最新应用。 但是,这些模型缺乏解答反事实问题的能力,而反事实问题随着这些模型被用于为有针对性的干预提供信息而越来越重要。 在这项工作中,我们的目标是填补这一空白。为此目的,我们首先开发一个以 Gumbel-Max 结构因果模型为基础的时间点过程的因果变薄模型。这个模型满足了一种理想的反事实单一性条件,足以确定薄化过程中的反事实动态。随后,由于观察到一个带有特定强度功能的时间点过程已经实现,我们开发了一种抽样算法,使用上述因果变薄模型和超定位标语来模拟在给定的替代强度功能下对时间点过程的反事实实现。 使用合成和实际流行病学数据进行的模拟实验表明,我们的算法所提供的反事实化结果可能提供宝贵的洞察力,以加强有针对性的干预。