论文标题
因果马赛克：通过非线性ICA和集合法的原因推论
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
论文作者
论文摘要
我们解决了将原因与双变量环境中的影响区分开的问题。基于非线性独立组件分析（ICA）的最新发展，我们训练允许非添加噪声的非参数一般非线性因果模型。此外，我们构建了一个合奏框架，即因果马赛克，该框架通过非线性模型的混合物建模因果对。我们将这种方法与有关人工和现实世界基准数据集的其他最新方法进行了比较，我们的方法显示了最先进的性能。
We address the problem of distinguishing cause from effect in bivariate setting. Based on recent developments in nonlinear independent component analysis (ICA), we train nonparametrically general nonlinear causal models that allow non-additive noise. Further, we build an ensemble framework, namely Causal Mosaic, which models a causal pair by a mixture of nonlinear models. We compare this method with other recent methods on artificial and real world benchmark datasets, and our method shows state-of-the-art performance.
