Learning a causal directed acyclic graph from data is a challenging task that
involves solving a combinatorial problem for which the solution is not always
identifiable. A new line of work reformulates this problem as a continuous
constrained optimization one, which is solved via the augmented Lagrangian
method. However, most methods based on this idea do not make use of
interventional data, which can significantly alleviate identifiability issues.
This work constitutes a new step in this direction by proposing a
theoretically-grounded method based on neural networks that can leverage
interventional data. We illustrate the flexibility of the
continuous-constrained framework by taking advantage of expressive neural
architectures such as normalizing flows. We show that our approach compares
favorably to the state of the art in a variety of settings, including perfect
and imperfect interventions for which the targeted nodes may even be unknown.

本文提出了一种新的方法，基于神经网络和流形变换，使用干预数据来学习因果有向无环图，该方法在多种情况下表现出与现有技术相媲美的灵活性和可行性。

干预数据的可微因果发现

Differentiable Causal Discovery from Interventional Data

We propose a novel score-based approach to learning a directed acyclic graph
(DAG) from observational data. We adapt a recently proposed continuous
constrained optimization formulation to allow for nonlinear relationships
between variables using neural networks. This extension allows to model complex
interactions while avoiding the combinatorial nature of the problem. In
addition to comparing our method to existing continuous optimization methods,
we provide missing empirical comparisons to nonlinear greedy search methods. On
both synthetic and real-world data sets, this new method outperforms current
continuous methods on most tasks, while being competitive with existing greedy
search methods on important metrics for causal inference.

本文提出了一种基于得分的方法，利用神经网络在连续约束优化理论的基础上，允许处理变量之间的非线性关系，用于学习有向无环图，相对于其他连续优化方法，这种方法在很多任务上表现更好，在因果推断的重要度量上与现有的贪婪搜索方法相比具有竞争力。在合成存储和真实世界数据集上进行了试验。