The goal of causal representation learning is to find a representation of data that consists of
causally related latent variables. We consider a setup where one has access to data from …

Identification based on higher moments has drawn increasing theoretical attention and been
widely adopted in empirical practice in macroeconometrics in the last two decades. This …

This study proposes a combination of a statistical identification approach with potentially
invalid short-run zero restrictions. The estimator shrinks towards imposed restrictions and …

Generalized method of moments estimators based on higher-order moment conditions
derived from independent shocks can be used to identify and estimate the simultaneous …

In this work, we explore Partitioned Independent Component Analysis (PICA), an extension
of the well-established Independent Component Analysis (ICA) framework. Traditionally, ICA …

We propose a statistical identification procedure for recursive structural vector
autoregressive (VAR) models that present a nonlinear dependence (at least) at the …

Linear non-Gaussian causal models postulate that each random variable is a linear function
of parent variables and non-Gaussian exogenous error terms. We study identification of the …

Tensors are ubiquitous in statistics and data analysis. The central object that links data
science to tensor theory and algebra is that of a model with latent variables. We provide an …

Recent development in identification methods utilizing higher-order moments have
advanced structural analysis in macroeconomics. This paper reviews prevailing …

We establish the identification of a specific shock in a structural vector autoregressive model
under the assumption that this shock is independent of the other shocks in the system …