Non-linear Causal Inference using Gaussianity Measures
We provide theoretical and empirical evidence for a type of asymmetry between causes and e ects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the e ects have the same distribution, we show that the distribution of...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/675246 |
| Acceso en línea: | http://hdl.handle.net/10486/675246 |
| Access Level: | acceso abierto |
| Palabra clave: | Causal inference Gaussianity of the residuals Cause-effect pairs Informática |
| Sumario: | We provide theoretical and empirical evidence for a type of asymmetry between causes and e ects that is present when these are related via linear models contaminated with additive non-Gaussian noise. Assuming that the causes and the e ects have the same distribution, we show that the distribution of the residuals of a linear t in the anti-causal direction is closer to a Gaussian than the distribution of the residuals in the causal direction. This Gaussianization e ect is characterized by reduction of the magnitude of the high-order cumulants and by an increment of the di erential entropy of the residuals. The problem of non-linear causal inference is addressed by performing an embedding in an expanded feature space, in which the relation between causes and e ects can be assumed to be linear. The e ectiveness of a method to discriminate between causes and e ects based on this type of asymmetry is illustrated in a variety of experiments using di erent measures of Gaussianity. The proposed method is shown to be competitive with state-of-the-art techniques for causal inference. |
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