论文标题
使用定量信息流进行成对因果发现的初步结果
Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow
论文作者
论文摘要
成对因果发现是确定因变量对的因果，反疗法，混杂或独立关系的任务。在过去的几年中，这项具有挑战性的任务不仅促进了旨在解决任务的新型机器学习模型的发现，而且还讨论了学习变量的因果方向如何使机器学习总体上受益。在本文中，我们表明定量信息流（QIF）是通常用于测量从系统到攻击者的信息泄漏的措施，显示出令人鼓舞的结果作为任务的功能。特别是，使用现实世界数据集的实验表明QIF统计与技术状态相关。我们的最初结果激发了QIF与因果关系的关系及其局限性的进一步询问。
Pairwise Causal Discovery is the task of determining causal, anticausal, confounded or independence relationships from pairs of variables. Over the last few years, this challenging task has promoted not only the discovery of novel machine learning models aimed at solving the task, but also discussions on how learning the causal direction of variables may benefit machine learning overall. In this paper, we show that Quantitative Information Flow (QIF), a measure usually employed for measuring leakages of information from a system to an attacker, shows promising results as features for the task. In particular, experiments with real-world datasets indicate that QIF is statistically tied to the state of the art. Our initial results motivate further inquiries on how QIF relates to causality and what are its limitations.
