Partial association measures and an application to qualitative regression

By Jean-Jacques Daudin. 1980

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In Statistics and Probability

Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “(2011) resorts to the characterization based on the concept of partial association of Daudin (1980) which says that X ⊥⊥ Y |Z if and only if E[fg] = 0 for all suitable f chosen to be a function from Z to X and for all suitable g chosen to be a function from Z to Y (see, e.g., Zhang et al. (2011; Lem”
Kernel-based Conditional Independence Test and Application in Causal Discovery (2011, background): “Lemma 2 [Characterization based on partial association (Daudin, 1980)] The following conditions are equivalent to each other.”
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations (2021, background): “The notion of the weak-conditional mutual information comes from weak-conditional independence [8, 12, 13], which is deﬁned as the case when P X,Y = E P Z P X | Z P Y | Z .”
A Permutation-Based Kernel Conditional Independence Test (2014, background): “independence defined in terms of the partial association under all square-integrable functions relating the variablesX , Y , and Z (Daudin, 1980).”
Testing conditional independence using maximal nonlinear conditional correlation (2010, background): “As mentioned in Daudin [2], X and Y are conditionally independent given Z means that for every f (X,Z) and g(Y,Z) such that Ef 2(X,Z) and Eg2(Y,Z) are finite E(f (X,Z)g(Y,Z)|Z) = E(f (X,Z)|Z)E(g(Y,Z)|Z).”
Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery (2017, background): “[24] The following conditions are equivalent: 1.”
A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data (2014, unknown): “ (2011) make the following comment regarding their use of Reproducing Kernel Hilbert Spaces (RKHS): "Alternatively, Daudin (1980) gives the characterization of CI by explicitly enforcing the uncorrelatedness of functions in suitable spaces, …”