论文标题
PC算法的自动超参数选择
Automated Hyperparameter Selection for the PC Algorithm
论文作者
论文摘要
PC算法使用条件独立性测试需要预先指定的I $α$水平的因果关系。但是，PC是无监督的，因此我们不能使用传统的交叉验证调整$α$。因此，我们提出AUTOPC，这是一个快速的程序，可直接针对用户选择的度量标准优化$α$。我们特别强迫PC通过在恢复图上执行第二次运行来仔细检查其输出。我们选择最终输出，将两次运行之间的稳定性最大化。 AUTOPC始终在多个指标上表现出色的状态。
The PC algorithm infers causal relations using conditional independence tests that require a pre-specified Type I $α$ level. PC is however unsupervised, so we cannot tune $α$ using traditional cross-validation. We therefore propose AutoPC, a fast procedure that optimizes $α$ directly for a user chosen metric. We in particular force PC to double check its output by executing a second run on the recovered graph. We choose the final output as the one which maximizes stability between the two runs. AutoPC consistently outperforms the state of the art across multiple metrics.
