通过漫反射正则化进行因果学习

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2019-01-01
Steven M Hill, Chris J Oates, Duncan A Blythe, Sach Mukherjee
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引用次数: 0

摘要

本文将因果结构估算作为一项机器学习任务。其思路是将变量间因果关系的指标视为 "标签",并利用相关变量的可用数据为标签任务提供特征。背景科学知识或任何可用的干预数据可为某些因果关系提供标签,而其余的则被视为无标签。为了说明关键思路,我们在流形正则化框架内开发了一种基于距离的方法(基于双变量直方图)。我们展示了三个不同生物数据集(包括可通过实验干预验证因果效应的例子)的实证结果,这些结果共同证明了该方法的有效性和通用性,以及从用户角度看它的简便性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Causal Learning via Manifold Regularization.

Causal Learning via Manifold Regularization.

Causal Learning via Manifold Regularization.

Causal Learning via Manifold Regularization.

This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as 'labels' and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interventional data provide labels on some causal relationships and the remainder are treated as unlabelled. To illustrate the key ideas, we develop a distance-based approach (based on bivariate histograms) within a manifold regularization framework. We present empirical results on three different biological data sets (including examples where causal effects can be verified by experimental intervention), that together demonstrate the efficacy and general nature of the approach as well as its simplicity from a user's point of view.

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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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