Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders
Abstract
We propose a kernel method to identify finite mixtures of nonparametric product distributions. It is based on a Hilbert space embedding of the joint distribution. The rank of the constructed tensor is equal to the number of mixture components. We present an algorithm to recover the components by partitioning the data points into clusters such that the variables are jointly conditionally independent given the cluster. This method can be used to identify finite confounders.
Cite
Text
Sgouritsa et al. "Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders." Conference on Uncertainty in Artificial Intelligence, 2013.Markdown
[Sgouritsa et al. "Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders." Conference on Uncertainty in Artificial Intelligence, 2013.](https://mlanthology.org/uai/2013/sgouritsa2013uai-identifying/)BibTeX
@inproceedings{sgouritsa2013uai-identifying,
title = {{Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders}},
author = {Sgouritsa, Eleni and Janzing, Dominik and Peters, Jonas and Schölkopf, Bernhard},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2013},
url = {https://mlanthology.org/uai/2013/sgouritsa2013uai-identifying/}
}