在未知因果图下，通过主动探索结构来优化目标变量。
Graph Agnostic Causal Bayesian Optimisation
- 不依赖已知因果图，主动发现影响目标的关键因果结构。
- 在模拟和真实场景中均优于现有基线算法。
- 适合需要干预优化但因果关系未知的科研与工业场景。
我们研究在未知因果图上通过一系列软或硬干预全局优化目标变量的问题。该问题被形式化为因果贝叶斯优化（Causal Bayesian Optimisation, CBO）。针对因果图未知的情况，我们在两种设定下研究累积遗憾目标：带有硬干预的结构因果模型，以及带有软干预的函数网络。我们提出图无关因果贝叶斯优化（Graph Agnostic Causal Bayesian Optimisation, GACBO），一种能主动发现对最优回报有贡献的因果结构的算法。GACBO在利用高回报动作与探索因果结构及函数之间寻求平衡。据我们所知，这是首个在因果图未知或部分已知条件下研究累积遗憾目标的因果贝叶斯优化工作。实验表明，所提算法在模拟实验和真实应用中均优于基线方法。
原文摘要 · Abstract (English)
We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimising the target variable associated with a causal graph is formalised as Causal Bayesian Optimisation (CBO). We study the CBO problem under the cumulative regret objective with unknown causal graphs for two settings, namely structural causal models with hard interventions and function networks with soft interventions. We propose Graph Agnostic Causal Bayesian Optimisation (GACBO), an algorithm that actively discovers the causal structure that contributes to achieving optimal rewards. GACBO seeks to balance exploiting the actions that give the best rewards against exploring the causal structures and functions. To the best of our knowledge, our work is the first to study causal Bayesian optimization with cumulative regret objectives in scenarios where the graph is unknown or partially known. We show our proposed algorithm outperforms baselines in simulated experiments and real-world applications.
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