Causal graph discovery is a significant problem with applications across various disciplines. However, with observational data alone, the underlying causal graph can only be recovered up to its Markov equivalence class, and further assumptions or interventions are necessary to narrow down the true graph. This work addresses the causal discovery problem under the setting of stochastic interventions with the natural goal of minimizing the number of interventions performed. We propose the following stochastic intervention model which subsumes existing adaptive noiseless interventions in the literature while capturing scenarios such as fat-hand interventions and CRISPR gene knockouts: any intervention attempt results in an actual intervention on a random subset of vertices, drawn from a distribution dependent on attempted action. Under this model, we study the two fundamental problems in causal discovery of verification and search and provide approximation algorithms with polylogarithmic competitive ratios and provide some preliminary experimental results.

翻译：因果图发现是一个重要问题，广泛应用于各个学科。然而，仅凭观测数据，潜在的因果图只能恢复至其马尔可夫等价类，需要进一步假设或干预才能缩小真实图的范围。本研究针对随机干预设置下的因果发现问题，以最小化干预次数为自然目标。我们提出以下随机干预模型，该模型既涵盖了现有文献中的自适应无噪声干预，又能够刻画诸如"胖手干预"和CRISPR基因敲除等场景：任何干预尝试都会导致对随机顶点子集的实际干预，该子集基于尝试动作的分布随机抽取。在此模型下，我们研究了因果发现中的验证与搜索两个基本问题，提出了具有多对数竞争比的近似算法，并给出了初步实验结果。