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.

本文提出了一种新的梯度干预目标方法，用于实验性设计的因果发现，该方法在模拟和现实世界数据集上进行了广泛的实验，并表现出与竞争基线相当的性能，甚至在低数据情况下超越了它们。