This paper studies the problem of performing a sequence of optimal interventions in a causal dynamical system where both the target variable of interest and the inputs evolve over time. This problem arises in a variety of domains e.g. system biology and operational research. Dynamic Causal Bayesian Optimization (DCBO) brings together ideas from sequential decision making, causal inference and Gaussian process (GP) emulation. DCBO is useful in scenarios where all causal effects in a graph are changing over time. At every time step DCBO identifies a local optimal intervention by integrating both observational and past interventional data collected from the system. We give theoretical results detailing how one can transfer interventional information across time steps and define a dynamic causal GP model which can be used to quantify uncertainty and find optimal interventions in practice. We demonstrate how DCBO identifies optimal interventions faster than competing approaches in multiple settings and applications.
翻译:本文研究在因果动态系统中进行一系列最佳干预的问题,在这种系统中,目标利益变量和投入随时间变化而变化,这个问题出现在各种领域,例如系统生物学和业务研究。动态Causal Bayesian Optimination(DCBO)汇集了从顺序决策、因果推断和Gaussian进程(GP)模拟中得出的各种想法。DCBO在图表中的所有因果效应随着时间的推移而变化的情景中非常有用。DCBO每一步都通过综合从系统中收集的观测和过去干预数据,确定一种地方最佳干预。我们从理论上得出结果,详细说明一个人如何在时间步骤之间传递干预信息,并界定动态因果GP模式,用于量化不确定性并在实践中找到最佳干预措施。我们展示DCBO如何在多种环境和应用中比相互竞争的方法更快地确定最佳干预。