مقاله Least-squares independence regression for non-linear causal inference under non-Gaussian noise : تی پی بین

خلاصه مقاله

The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive noise model through the minimization of an estimator of the squared-loss mutual information between inputs and residuals. A notable advantage of LSIR is that tuning parameters such as the kernel width and the regularization parameter can be naturally optimized by cross-validation, allowing us to avoid overfitting in a data-dependent fashion. Through experiments with real-world datasets, we show that LSIR compares favorably with a state-of-the-art causal inference method.

نویسندگان

Makoto Yamada [ 701 1st Ave., Sunnyvale, CA, 94089, USA 2. 2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan ]

Masashi Sugiyama [ 701 1st Ave., Sunnyvale, CA, 94089, USA 2. 2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan ]

Jun Sese [ 701 1st Ave., Sunnyvale, CA, 94089, USA 2. 2-12-1, O-okayama, Meguro-ku, Tokyo, 152-8552, Japan ]

کلمات کلیدی

Causal inference Non linear Non Gaussian Squared loss mutual information Least squares independence regression

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Machine Learning 2013

Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems. The journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted. All papers describe the supporting evidence in ways that can be verified or replicated by other researchers. The papers also detail the learning component clearly and discuss assumptions regarding knowledge representation and the performance task.

حوزه های تحت پوشش : Artificial Intelligence, Control, Robotics, Mechatronics, Computing Methodologies,Simulation and Modeling