# Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

arXiv: https://arxiv.org/abs/1309.6860 · HTML: https://mlsanity.com/paper/1309.6860/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2013.

## Built on by (most cited first, 1 of 1)

- [Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence](https://mlsanity.com/paper/2606.07914/) (2026)

## Abstract (arXiv)

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.