Causal Transfer Learning
Causal Transfer Learning
复制标题
因果迁移学习
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Mooij
中科院分区：
文献类型：
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作者：
Sara Magliacane;T. V. Ommen;Tom Claassen;S. Bongers;Philip Versteeg;J. Mooij
迁移学习和因果推理的一个重要目标是在测试集和训练集的分布不同时做出准确的预测。这种分布偏移可能由于对数据生成过程的外部干预而发生，导致分布的某些方面发生变化，而其他方面保持不变。我们考虑一类因果迁移学习问题，其中给出了对应于不同外部干预的多个训练集，任务是预测目标变量的分布，给出对系统进行新（尚未看到）干预的其他变量的测量。我们提出了一种解决这些问题的方法，利用因果推理，但既不依赖于因果图的先验知识，也不依赖于干预措施的类型及其目标。我们评估模拟和真实的世界数据的方法，发现它优于一个标准的预测方法，忽略了分布的转变。
An important goal in both transfer learning and causal inference is to make accurate predictions when the distribution of the test set and the training set(s) differ. Such a distribution shift may happen as a result of an external intervention on the data generating process, causing certain aspects of the distribution to change, and others to remain invariant. We consider a class of causal transfer learning problems, where multiple training sets are given that correspond to different external interventions, and the task is to predict the distribution of a target variable given measurements of other variables for a new (yet unseen) intervention on the system. We propose a method for solving these problems that exploits causal reasoning but does neither rely on prior knowledge of the causal graph, nor on the the type of interventions and their targets. We evaluate the method on simulated and real world data and find that it outperforms a standard prediction method that ignores the distribution shift.
DOI：
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发表时间：
2016-06
期刊：
JMLR workshop and conference proceedings
影响因子：
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作者：
Mingming Gong;Kun Zhang;Tongliang Liu;D. Tao;C. Glymour;B. Scholkopf
通讯作者：
Mingming Gong;Kun Zhang;Tongliang Liu;D. Tao;C. Glymour;B. Scholkopf