Pairwise cluster comparison for learning latent variable models

Nuaman Asbeh, Boaz Lerner

Research output: Contribution to journalConference articlepeer-review


Learning a latent variable model (LVM) exploits values of the measured variables as manifested in the data to causal discovery. Because the challenge in learning an LVM is similar to that faced in unsupervised learning, where the number of clusters and the classes that are represented by these clusters are unknown, we link causal discovery and clustering. We propose the concept of pairwise cluster comparison (PCC), by which clusters of data points are compared pairwise to associate changes in the observed variables with changes in their ancestor latent variables and thereby to reveal these latent variables and their causal paths of influence, and the learning PCC (LPCC) algorithm that identifies PCCs and uses them to learn an LVM. LPCC is not limited to linear or latent-tree models. It returns a pattern of the true graph or the true graph itself if the graph has serial connections or not, respectively. The complete theoretical foundation to PCC, the LPCC algorithm, and its experimental evaluation are given in [Asbeh and Lerner, 2016a,b], whereas, here, we only introduce and promote them. The LPCC code and evaluation results are available online.

Original languageEnglish
Pages (from-to)29-38
Number of pages10
JournalCEUR Workshop Proceedings
StatePublished - 1 Jan 2016
Event2016 UAI Workshop on Causation: Foundation to Application, UAI-CFA 2016, co-located with the 32nd Conference on Uncertainty in Artificial Intelligence, UAI 2016 - Jersey City, United States
Duration: 29 Jun 2016 → …

ASJC Scopus subject areas

  • Computer Science (all)


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