Supervised Causal Learning (SCL) aims to learn causal relations from observational data by accessing previously seen datasets associated with ground truth causal relations. This paper presents a first attempt at addressing a fundamental question: What are the benefits from supervision and how does it benefit? Starting from seeing that SCL is not better than random guessing if the learning target is non-identifiable a priori, we propose a two-phase paradigm for SCL by explicitly considering structure identifiability. Following this paradigm, we tackle the problem of SCL on discrete data and propose ML4C. The core of ML4C is a binary classifier with a novel learning target: it classifies whether an Unshielded Triple (UT) is a v-structure or not. Specifically, starting from an input dataset with the corresponding skeleton provided, ML4C orients each UT once it is classified as a v-structure. These v-structures are together used to construct the final output. To address the fundamental question of SCL, we propose a principled method for ML4C featurization: we exploit the vicinity of a given UT (i.e., the neighbors of UT in skeleton), and derive features by considering the conditional dependencies and structural entanglement within the vicinity. We further prove that ML4C is asymptotically correct. Last but foremost, thorough experiments conducted on benchmark datasets demonstrate that ML4C remarkably outperforms other state-of-the-art algorithms in terms of accuracy, reliability, robustness and tolerance. In summary, ML4C shows promising results on validating the effectiveness of supervision for causal learning. Our codes are publicly available at https://github.com/microsoft/ML4C.

翻译：监督式因果学习（SCL）旨在通过访问先前观察到的、带有真实因果关联的数据集，从观测数据中学习因果关系。本文首次尝试解决一个基本问题：监督带来了哪些益处，以及它如何实现这些益处？从观察到若学习目标先验不可识别、则SCL并不优于随机猜测这一事实出发，我们通过显式考虑结构可识别性，提出了一种用于SCL的两阶段范式。遵循该范式，我们解决了离散数据上的SCL问题，并提出了ML4C。ML4C的核心是一个具有新颖学习目标的二分类器：它判断一个无屏蔽三元组（UT）是否为V型结构。具体而言，从提供相应骨架的输入数据集开始，ML4C在将每个UT分类为V型结构后对其进行定向。这些V型结构共同用于构建最终输出。为解决SCL的基本问题，我们提出了一种原则性的ML4C特征化方法：我们利用给定UT的邻域（即骨架中UT的邻居），通过考虑邻域内的条件依赖性和结构纠缠来推导特征。我们进一步证明了ML4C是渐近正确的。最后但同样重要的是，在基准数据集上进行的充分实验表明，ML4C在准确性、可靠性、鲁棒性和容错性方面显著优于其他最先进算法。综上所述，ML4C在验证监督对因果学习的有效性方面展现了令人鼓舞的结果。我们的代码已公开于https://github.com/microsoft/ML4C。