Recovering Latent Variables by Matching
We propose an optimal-transport-based matching method to nonparametrically estimate linear models with independent latent variables. The method consists in generating pseudo-observations from the latent variables, so that the Euclidean distance between the model's predictions and their matched counterparts in the data is minimized. We show that our nonparametric estimator is consistent, and we document that it performs well in simulated data. We apply this method to study the cyclicality of permanent and transitory income shocks in the Panel Study of Income Dynamics. We find that the dispersion of income shocks is approximately acyclical, whereas the skewness of permanent shocks is procyclical. By comparison, we find that the dispersion and skewness of shocks to hourly wages vary little with the business cycle.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrix
Discovering the causal relationship via recovering the directed acyclic graph (DAG) structure from the observed data is a well-known challenging combinatorial problem. When there are latent variables, the problem becomes…
Score-based Causal Representation Learning with Interventions
This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are: (i) recovering the unknown linear trans…
Representation LearningvalidIdentifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing
Causal representation learning (CRL) aims to identify the underlying latent variables from high-dimensional observations, even when variables are dependent with each other. We study this problem for latent variables that…
Representation LearningCommunity Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables
Empirical observations suggest that in practice, community membership does not completely explain the dependency between the edges of an observation graph. The residual dependence of the graph edges are modeled in this p…
Community DetectionStochastic Block ModelIdentification of Causal Direction under an Arbitrary Number of Latent Confounders
Recovering causal structure in the presence of latent variables is an important but challenging task. While many methods have been proposed to handle it, most of them require strict and/or untestable assumptions on the c…