Nonlinear directed acyclic structure learning with weakly additive noise models

By Robert E. Tillman, Arthur Gretton, Peter Spirtes. 2009

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In Causal Inference & Discovery

Citations in the corpus

Identifiability of Causal Graphs using Functional Models (2011, methodology): “two-variable case described above, practical methods for causal inference using ANMs have been suggested for the multivariate case [Hoyer et al., 2009, Zhang and Hyvarinen, 2009, Mooij et al., 2009, Tillman et al., 2009]: whenever a graph models the data the method infer this structure as the causal”
Kernel-based Conditional Independence Test and Application in Causal Discovery (2011, methodology): “Sun et al. (2007) and Tillman et al. (2009) pro-pose to use CI PERM for CI testing in PC. Based on the promising results of KCI-test, we propose to also apply it to causal inference.”
A Kernel Test for Three-Variable Interactions (2013, methodology): “A number of algorithms have since extended the basic PC algorithm to arbitrary probability distributions over multivariate random variables [16, 17, 18], by using nonparametric kernel independence tests [19] and conditional dependence tests [20, 18].”
Identifiability of Gaussian structural equation models with equal error variances (2012, methodology): “A related approach has been suggested by Tillman et al. [2010]. For consistency in the first step one necessarily requires a version of the strong faithfulness assumption, which can be very restrictive [Uhler et al.”
Identiﬁability of Causal Graphs using Functional Models (methodology): “…case described above, practical methods for causal inference using ANMs have been suggested for the multivariate case [Hoyer et al., 2009, Zhang and Hyv¨arinen, 2009, Mooij et al., 2009, Tillman et al., 2009]: whenever a graph models the data the method infer this structure as the causal graph.”
Permutation-based Causal Inference Algorithms with Interventions (2017, methodology): “For this dataset we compared the GIES results reported in [9] with Algorithm 2 using both, a linear Gaussian and a kernel-based independence criterium [6, 24].”