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 squared-loss 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.
Cite
Text
Yamada and Sugiyama. "Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference Under Non-Gaussian Noise." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7655Markdown
[Yamada and Sugiyama. "Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference Under Non-Gaussian Noise." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/yamada2010aaai-dependence/) doi:10.1609/AAAI.V24I1.7655BibTeX
@inproceedings{yamada2010aaai-dependence,
title = {{Dependence Minimizing Regression with Model Selection for Non-Linear Causal Inference Under Non-Gaussian Noise}},
author = {Yamada, Makoto and Sugiyama, Masashi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2010},
pages = {643-648},
doi = {10.1609/AAAI.V24I1.7655},
url = {https://mlanthology.org/aaai/2010/yamada2010aaai-dependence/}
}