Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system's causal structure uniquely. While conducting interventions (i.e., experiments) can improve the identifiability, such samples are usually challenging and expensive to obtain. Hence, experimental design approaches for causal discovery aim to minimize the number of interventions by estimating the most informative intervention target. In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines, surpassing them in the low-data regime.

翻译：从数据中推断因果结构是科学领域一项具有根本重要性的挑战性任务。观测数据通常不足以唯一确定系统的因果结构。虽然进行干预（即实验）可以提高可辨识性，但这类样本通常难以获取且成本高昂。因此，因果发现的实验设计方法旨在通过估计最具信息量的干预目标来最小化干预次数。本研究提出了一种新颖的基于梯度的干预目标选择方法（简称GIT），该方法"信任"梯度导向的因果发现框架中的梯度估计器，为干预采集函数提供信号。我们在模拟数据集和真实世界数据集上开展了大量实验，结果表明GIT在性能上与竞争基线方法相当，在低数据场景下甚至超越它们。