Causality pairwise inference datasets. Replication Data for: "Learning Functional Causal Models with Generative Neural Networks"

By Olivier Goudet. 2017

116 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges (2018, background): “as part of the loss function [12, 11], modules that add additional capabilities [49, 43], or as part of the architecture structure, in terms of operations between layers [32, 55, 33, 19].”
Learning Neural Causal Models from Unknown Interventions (2019, background): “…based on observational data (Chickering, 2002; Tsamardinos et al., 2006; Goudet et al., 2017; Hauser & Bühlmann, 2012; Spirtes et al., 2000; Sun et al., 2007; Zhang et al., 2012; Shimizu et al., 2006; Hoyer et al., 2009; Daniusis et al., 2012; Budhathoki & Vreeken, 2017; Kalainathan et al., 2018).”
Masked Gradient-Based Causal Structure Learning (2019, methodology): “Other related meth-ods include Goudet et al. [9] that uses generative neural networks for functional causal modeling with an initial graph skeleton, Ke et al. [13] that uses logistic sigmoid functions to sample binary adjacency matrices in the presence of unknown interventions, Ng et al. [21] that…”
Deep Structural Causal Models for Tractable Counterfactual Inference (2020, background): “For example, research has studied the use of causality for disentanglement [6, 7], causal discovery [8, 9], and for deriving causality-inspired explanations [10, 11] or data augmentations [12].”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “CMS [152] 2014 low no Bi NO TEARS [267] 2018 low yes no DAG CGNN [75] 2018 low yes no DAG Graphite [83] 2019 low/medium no no UG SAM [122] 2019 low/medium yes no DAG DAG-GNN [262] 2019 low yes no DAG GAE [177] 2019 low yes no DAG NO BEARS [142] 2019 low/medium/high yes no DAG Meta-Transfer [19] 2019”
Causal Inference via Kernel Deviance Measures (2018, methodology): “ANM PNL RCC Best from [16] CGNN [7] KCDC-D1 KCDC-D2”
Causal Discovery Toolbox: Uncover causal relationships in Python (2019, background): “, such as CGNN or SAM (Goudet et al., 2017; Kalainathan et al., 2018).”