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中具有根本重要性的一项具有挑战性的任务。观测数据往往不足以独特地确定系统的因果结构。虽然进行干预(即实验)可以提高可识别性,但此类样本通常具有挑战性,而且获取费用昂贵。因此,因果发现实验设计方法的目的是通过估计信息最丰富的干预目标,最大限度地减少干预措施的数量。在这项工作中,我们提出了一种新的基于渐进的干预定向方法,缩略式GIT,即“信任”基于梯度的因果发现框架的梯度估计符,为获取干预功能提供信号。我们在模拟和现实世界数据集中提供了广泛的实验,并表明GIT在与竞争性基线同等的基础上运行,在低数据系统中超过了这些基准。</s>