13 citations · 22 across the 7 of their papers we have counts for
5 papers · 1 filter
Causal Autoregressive Flows
Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech +1
Two apparently unrelated fields -- normalizing flows and causality -- have recently received considerable attention in the machine learning community. In this work, we highlight an…
Autoregressive flow-based causal discovery and inference
Ricardo Pio Monti, Ilyes Khemakhem, Aapo Hyvarinen
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual…
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma +1
We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional ene…
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen
We consider the problem of inferring causal relationships between two or more passively observed variables. While the problem of such causal discovery has been extensively studied…
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
Ricardo Pio Monti, Aapo Hyvärinen
Connectivity estimation is challenging in the context of high-dimensional data. A useful preprocessing step is to group variables into clusters, however, it is not always clear how…