论文标题
因果结构学习：基于随机图的贝叶斯方法
Causal Structure Learning: a Bayesian approach based on random graphs
论文作者
论文摘要
随机图是一个随机对象，在图形空间中采用其值。我们利用图的表达性，以模拟给定变量集中因果关系的不确定性。我们采用贝叶斯的观点，以通过互动和与因果环境学习来捕获因果结构。我们在两种不同的情况下测试我们的方法，实验主要证实我们的技术可以学习因果结构。此外，针对第一个测试场景提出的实验和结果证明了我们学习因果结构以及最佳作用的方法的有用性。另一方面，第二个实验表明，我们的提案设法学习了具有不同大小和不同因果结构的几个任务的基本因果结构。
A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures.
