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

Understand

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.