We focus on the extension of bivariate causal learning methods into multivariate problem settings in a systematic manner via a novel framework. It is purposive to augment the scale to which bivariate causal discovery approaches can be applied since contrast to traditional causal discovery methods, bivariate methods render estimation in the form of a causal Directed Acyclic Graph (DAG) instead of its complete partial directed acyclic graphs (CPDAGs). To tackle the problem, an auxiliary framework is proposed in this work so that together with any bivariate causal inference method, one could identify and estimate causal structure over variables more than two from observational data. In particular, we propose a local graphical structure in causal graph that is identifiable by a given bivariate method, which could be iteratively exploited to discover the whole causal structure under certain assumptions. We show both theoretically and experimentally that the proposed framework can achieve sound results in causal learning problems.

翻译：我们聚焦于通过一种新颖框架，系统地将双变量因果学习方法扩展到多变量问题情境。该框架旨在扩大双变量因果发现方法的适用范围，因为与传统因果发现方法相比，双变量方法以因果有向无环图（DAG）而非完整偏有向无环图（CPDAGs）的形式进行估计。为解决这一问题，本文提出一个辅助框架，使其能与任意双变量因果推断方法结合，从观测数据中识别并估计超过两个变量间的因果结构。具体而言，我们提出因果图中可由给定双变量方法识别的局部图结构，在特定假设下可迭代利用该结构发现整体因果结构。理论分析与实验结果表明，所提框架能在因果学习问题中取得有效结果。