用信息熵最小化方法，首次实现多节点因果图的可识别性推断。
Entropic Causal Inference: Graph Identifiability
- 基于双变量熵检验判断祖先关系，构建通用图的逐层剥离算法。
- 在多种合成数据上优于已有方法，真实数据也验证了有效性。
- 适合需要从观测数据中推断因果结构的研究者使用。
熵因果推断是一种从观测数据中学习两个变量间因果图的新框架，通过寻找信息论意义下最简单的结构解释（即熵最小的模型）来实现。本文首先在放宽假设条件下扩展了双变量场景下的因果图可识别性结果；随后首次提出基于熵方法对多节点因果图的学习可识别性结论。该方法利用源节点与其后代之间的祖先关系可通过双变量熵检验确定的特性，设计了一个可靠的顺序剥除算法以适用于一般图结构，并提出了一个针对小图的启发式算法，表现出优异的实证性能。我们在多种模型生成的合成数据上严格评估了算法表现，观察到对先前方法的改进；最后在真实数据集上进行了测试，进一步验证了其有效性。
原文摘要 · Abstract (English)
Entropic causal inference is a recent framework for learning the causal graph between two variables from observational data by finding the information-theoretically simplest structural explanation of the data, i.e., the model with smallest entropy. In our work, we first extend the causal graph identifiability result in the two-variable setting under relaxed assumptions. We then show the first identifiability result using the entropic approach for learning causal graphs with more than two nodes. Our approach utilizes the property that ancestrality between a source node and its descendants can be determined using the bivariate entropic tests. We provide a sound sequential peeling algorithm for general graphs that relies on this property. We also propose a heuristic algorithm for small graphs that shows strong empirical performance. We rigorously evaluate the performance of our algorithms on synthetic data generated from a variety of models, observing improvement over prior work. Finally we test our algorithms on real-world datasets.
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