We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target and multi-target settings across a number of synthetic datasets.

翻译：针对巴伊西亚州的最佳实验设计问题,我们引入了一种基于梯度的方法,在批量设置中学习因果模型 -- -- 这是从有限数据中得出因果发现的关键组成部分,因为干预可能成本高或风险大。现有方法依靠贪婪近似值来构建一批实验,同时使用黑盒方法优化单一目标国家对的干预。在这项工作中,我们完全处理黑盒优化技术和贪婪的惯性,并提议一个概念上简单的端对端梯度优化程序,以获得一套最佳干预目标国家对子。这种程序使得设计空间的参数化能够有效地优化对多目标国家干预的组合,迄今为止,由于这种组合的复杂性,还没有探索过这一架构。我们证明,我们所提议的方法在许多合成数据集中超越了单一目标和多目标环境的基线和现有采购战略。