论文标题
用于公用事业最大化剂的因果特征学习
Causal Feature Learning for Utility-Maximizing Agents
论文作者
论文摘要
从低级数据中发现高级因果关系是一个重要且具有挑战性的问题，在自然和社会科学中经常出现。在一系列论文中，Chalupka等人。 （2015，2016a，2016b，2017年）为因果特征学习（CFL）制定了一个程序，以使该任务自动化。我们认为，如果在务实的考虑因素有利于它的情况下，CFL不建议过于粗糙，并建议在务实考虑反对它的情况下粗糙。我们提出了一种新技术，务实的因果特征学习（PCFL），该技术以有用和直观的方式扩展了原始的CFL算法。我们表明，PCFL具有与原始CFL算法相同的吸引力理论特性。我们通过理论分析和实验比较两种方法的性能。
Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka et al. (2015, 2016a, 2016b, 2017) develop a procedure for causal feature learning (CFL) in an effort to automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments.
