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

Bernhard Schoelkopf; Dominik Janzing; Eleni Sgouritsa; Jonas Peters

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1309.6860 v1 pith:ECPX4WJI submitted 2013-09-26 cs.LG cs.AIstat.ML

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

Eleni Sgouritsa , Dominik Janzing , Jonas Peters , Bernhard Schoelkopf This is my paper

classification cs.LG cs.AIstat.ML

keywords finitecomponentsconfoundersdistributionsidentifymethodmixturesnonparametric

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

0 comments

read the original 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.

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence
stat.ML 2026-06 unverdicted novelty 7.0

Marginal independence enables identifiability of components and mixing matrix in unlabeled mixtures, with a PM-MMD estimator shown to converge uniformly under approximate independence.