Empirical likelihood for linear structural equation models with dependent errors

Y. Samuel Wang, Mathias Drton

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


We consider linear structural equation models that are associated with mixed graphs. The structural equations in these models only involve observed variables, but their idiosyncratic error terms are allowed to be correlated and non-Gaussian. We propose empirical likelihood procedures for inference and suggest several modifications, including a profile likelihood, in order to improve tractability and performance of the resulting methods. Through simulations, we show that when the error distributions are non-Gaussian, the use of empirical likelihood and the proposed modifications may increase statistical efficiency and improve assessment of significance.

Original languageEnglish
Pages (from-to)434-447
Number of pages14
Issue number1
StatePublished - 2017
Externally publishedYes


  • Causal inference
  • Empirical likelihood
  • Graphical model
  • Structural equation model


Dive into the research topics of 'Empirical likelihood for linear structural equation models with dependent errors'. Together they form a unique fingerprint.

Cite this