Dynamic Causal Bayesian Optimization
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.
Code (1)
Tasks
Bayesian OptimizationCausal InferenceDecision MakingSequential Decision MakingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multi-Objective Causal Bayesian Optimization
In decision-making problems, the outcome of an intervention often depends on the causal relationships between system components and is highly costly to evaluate. In such settings, causal Bayesian optimization (CBO) can e…
Bayesian OptimizationDecision MakingModel-based Causal Bayesian Optimization
In Causal Bayesian Optimization (CBO), an agent intervenes on an unknown structural causal model to maximize a downstream reward variable. In this paper, we consider the generalization where other agents or external even…
Bayesian OptimizationcounterfactualmodelModel-based Causal Bayesian Optimization
How should we intervene on an unknown structural equation model to maximize a downstream variable of interest? This setting, also known as causal Bayesian optimization (CBO), has important applications in medicine, ecolo…
Bayesian OptimizationmodelExtending Multi-Source Bayesian Optimization With Causality Principles
Multi-Source Bayesian Optimization (MSBO) serves as a variant of the traditional Bayesian Optimization (BO) framework applicable to situations involving optimization of an objective black-box function over multiple infor…
Dimensionality ReductionTransferring Information Across Interventions in Causal Bayesian Optimization
Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money. In its standard form, it treats the variables we control as plain inputs to a…