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：我们提出了一种基于梯度的方法，用于解决批量设置中学习因果模型的贝叶斯最优实验设计问题——这是从有限数据中进行因果发现的关键组成部分，其中干预可能代价高昂或具有风险。现有方法依赖于贪婪近似来构建一批实验，同时使用黑箱方法优化单个目标-状态对以进行干预。在这项工作中，我们完全摒弃了黑箱优化技术和贪婪启发式方法，转而提出一种概念简单的端到端梯度优化过程，以获取一组最优的干预目标-状态对。该过程能够参数化设计空间，从而高效地优化一批多目标-状态干预，这是由于其复杂性而此前尚未被探索过的设置。我们证明，在多个合成数据集上，我们提出的方法在单目标和多目标设置中均优于基线方法和现有采集策略。