Latent Composite Likelihood Learning for the Structured Canonical Correlation Model

TL;DR

This paper introduces a novel composite likelihood method for structured canonical correlation models to identify unanticipated latent variables and confounding effects without explicitly modeling all latent factors.

Contribution

It proposes a new structure learning approach using composite likelihood to detect unknown latent confounders in latent variable models, especially when some variables are only indirectly observed.

Findings

Effective detection of unanticipated latent variables.

Validated approach with synthetic data experiments.

Applied to large-scale NHS survey data.

Abstract

Latent variable models are used to estimate variables of interest quantities which are observable only up to some measurement error. In many studies, such variables are known but not precisely quantifiable (such as "job satisfaction" in social sciences and marketing, "analytical ability" in educational testing, or "inflation" in economics). This leads to the development of measurement instruments to record noisy indirect evidence for such unobserved variables such as surveys, tests and price indexes. In such problems, there are postulated latent variables and a given measurement model. At the same time, other unantecipated latent variables can add further unmeasured confounding to the observed variables. The problem is how to deal with unantecipated latents variables. In this paper, we provide a method loosely inspired by canonical correlation that makes use of background information concerning the "known" latent variables. Given a partially specified structure, it provides a structure learning approach to detect "unknown unknowns," the confounding effect of potentially infinitely many other latent variables. This is done without explicitly modeling such extra latent factors. Because of the special structure of the problem, we are able to exploit a new variation of composite likelihood fitting to efficiently learn this structure. Validation is provided with experiments in synthetic data and the analysis of a large survey done with a sample of over 100,000 staff members of the National Health Service of the United Kingdom.