Half-Trek Criterion for Identifiability of Latent Variable Models

          @misc{ scivideos_PIRSA:23040124,
            doi = {10.48660/23040124},
            url = {https://pirsa.org/23040124},
            author = {Drton, Mathias},
            keywords = {Quantum Foundations},
            language = {en},
            title = {Half-Trek Criterion for Identifiability of Latent Variable Models},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {apr},
            note = {PIRSA:23040124 see, \url{https://scivideos.org/pirsa/23040124}}
          }
          

Abstract

"Linear structural equation models relate random variables of interest via a linear equation system that features stochastic noise. The models are naturally represented by directed graphs whose edges indicate non-zero coefficients in the linear equations. In this talk I will report on progress on combinatorial conditions for parameter identifiability in models with latent (i.e., unobserved) variables. Identifiability holds if the coefficients associated with the edges of the graph can be uniquely recovered from the covariance matrix they define. Paper: https://doi.org/10.1214/22-AOS2221 or https://arxiv.org/abs/2201.04457"