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Rank-Based Causal Discovery for Post-Nonlinear Models
Grigor Keropyan
, David Strieder
,
Mathias Drton
Chair of Mathematical Statistics
Technical University of Munich
Munich Center for Machine Learning
Research output
:
Contribution to journal
›
Conference article
›
peer-review
4
Scopus citations
Overview
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Keyphrases
Additive Noise Model
33%
Bivariate
33%
Causal Direction
33%
Causal Discovery
100%
Causal Relationship
33%
Functional Parameters
33%
Functional Relationship
66%
Independence Test
66%
Interacting Variables
33%
Invariance
33%
Nonlinear Causal Discovery
33%
Nonlinear Function
33%
Nonlinear Functional
33%
Numerical Experiments
33%
Overfitting
33%
Popular
33%
Post-nonlinear
33%
Post-nonlinear Causal Model
33%
Post-nonlinear Model
100%
Ranking Method
100%
Structural Causal Models
66%
Unique Identifiability
33%
Mathematics
Additive Noise
33%
Bivariate Case
33%
Causal Discovery
100%
Causal Model
100%
Causal Relationship
33%
Functional Relation
66%
Identifiability
33%
Linear Functionals
33%
Nonlinear
33%
Nonlinear Function
33%
Nonlinear Model
100%
Numerical Experiment
33%
Residuals
66%
Computer Science
Causal Relationship
33%
Nonlinear Function
33%
Nonlinear Model
100%
Subclasses
100%
Neuroscience
Causal Model
100%