Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise

Abstract

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 minimization of an estimator of the squaredloss mutual information between inputs and residuals. A notable advantage of LSIR over existing approaches 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 the state-of-the-art causal inference method.

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Cite this paper

@inproceedings{Yamada2010DependenceMR, title={Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference under Non-Gaussian Noise}, author={Makoto Yamada and Masashi Sugiyama}, booktitle={AAAI}, year={2010} }