5 papers
Causal discovery under mean independence and linearity
Geert Mesters, Alvaro Ribot, Anna Seigal +1
Causal discovery methods such as LiNGAM identify causal structure from observational data by assuming mutually independent disturbances. This assumption is fragile: shared volatili…
Beyond independent component analysis: identifiability and algorithms
Alvaro Ribot, Anna Seigal, Piotr Zwiernik
Independent Component Analysis (ICA) is a classical method for recovering latent variables with useful identifiability properties. For independent variables, cumulant tensors are d…
Orthogonal eigenvectors and singular vectors of tensors
Alvaro Ribot, Anna Seigal, Piotr Zwiernik
The spectral theorem says that a real symmetric matrix has an orthogonal basis of eigenvectors and that, for a matrix with distinct eigenvalues, the basis is unique (up to signs).…
Decomposing tensors via rank-one approximations
Alvaro Ribot, Emil Horobet, Anna Seigal +1
Matrices can be decomposed via rank-one approximations: the best rank-one approximation is a singular vector pair, and the singular value decomposition writes a matrix as a sum of…
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models
Alvaro Ribot, Chandler Squires, Caroline Uhler
We consider the task of causal imputation, where we aim to predict the outcomes of some set of actions across a wide range of possible contexts. As a running example, we consider p…