A Recipe for Causal Graph Regression: Confounding Effects Revisited | Paper Citation Record

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This paper cites Deep evidential regression.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Deep evidential regression

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This paper cites Invariant Risk Minimization.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Invariant Risk Minimization

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This paper cites Information bottleneck for gaussian variables.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Information bottleneck for gaussian variables

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This paper cites Learning causally invariant representations for out-of-distribution generalization on graphs.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Learning causally invariant representations for out-of-distribution generalization on graphs

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This paper cites Does invariant graph learning via environment augmentation learn invariance? Advances in Neural Information Processing Systems, 36, 2024.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Does invariant graph learning via environment augmentation learn invariance? Advances in Neural Information Processing Systems, 36, 2024

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This paper cites Debiasing graph neural networks via learning disentangled causal substructure.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Debiasing graph neural networks via learning disentangled causal substructure

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This paper cites Generalizing graph neural networks on out-of-distribution graphs.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Generalizing graph neural networks on out-of-distribution graphs

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Domain-adversarial training of neural networks

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This paper cites Robust causal graph representation learning against confounding effects.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Robust causal graph representation learning against confounding effects

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This paper cites N., Duvenaud, D., Hern \'a ndez-Lobato, J.

A Recipe for Causal Graph Regression: Confounding Effects Revisited N., Duvenaud, D., Hern \'a ndez-Lobato, J

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This paper cites Good: A graph out-of-distribution benchmark.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Good: A graph out-of-distribution benchmark

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This paper cites R., and Liu, H.

A Recipe for Causal Graph Regression: Confounding Effects Revisited R., and Liu, H

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This paper cites Counterfactual learning on graphs: A survey.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Counterfactual learning on graphs: A survey

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This paper cites Junction tree variational autoencoder for molecular graph generation.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Junction tree variational autoencoder for molecular graph generation

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This paper cites P., Welling, M., et al.

A Recipe for Causal Graph Regression: Confounding Effects Revisited P., Welling, M., et al

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This paper cites Out-of-distribution generalization via risk extrapolation (rex).

A Recipe for Causal Graph Regression: Confounding Effects Revisited Out-of-distribution generalization via risk extrapolation (rex)

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This paper cites J., Paige, B., and Hern \'a ndez-Lobato, J.

A Recipe for Causal Graph Regression: Confounding Effects Revisited J., Paige, B., and Hern \'a ndez-Lobato, J

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This paper cites L., and Van Lint, H.

A Recipe for Causal Graph Regression: Confounding Effects Revisited L., and Van Lint, H

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This paper cites Ood-gnn: Out-of-distribution generalized graph neural network.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Ood-gnn: Out-of-distribution generalized graph neural network

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This paper cites Generative causal explanations for graph neural networks.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Generative causal explanations for graph neural networks

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This paper cites Parameterized explainer for graph neural network.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Parameterized explainer for graph neural network

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This paper cites Utilizing reinforcement learning and causal graph networks to address the intricate dynamics in financial risk prediction.

A Recipe for Causal Graph Regression: Confounding Effects Revisited Utilizing reinforcement learning and causal graph networks to address the intricate dynamics in financial risk prediction

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This paper cites A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View.

A Recipe for Causal Graph Regression: Confounding Effects Revisited A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Interpretable and generalizable graph learning via stochastic attention mechanism

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Representation Learning via Invariant Causal Mechanisms

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A Recipe for Causal Graph Regression: Confounding Effects Revisited and Weigend, A

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Representation Learning with Contrastive Predictive Coding

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Interpretation and identification of causal mediation

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Unresolved cited work

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Causal enhanced drug--target interaction prediction based on graph generation and multi-source information fusion

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A Recipe for Causal Graph Regression: Confounding Effects Revisited The perceptron: a probabilistic model for information storage and organization in the brain

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Unresolved cited work

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Enhancing out-of-distribution generalization on graphs via causal attention learning

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A Recipe for Causal Graph Regression: Confounding Effects Revisited and Saenko, K

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A Recipe for Causal Graph Regression: Confounding Effects Revisited No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets

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A Recipe for Causal Graph Regression: Confounding Effects Revisited and Zaslavsky, N

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A Recipe for Causal Graph Regression: Confounding Effects Revisited The information bottleneck method

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Principles of risk minimization for learning theory

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A Recipe for Causal Graph Regression: Confounding Effects Revisited F., Heinen, S

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A Recipe for Causal Graph Regression: Confounding Effects Revisited and Veitch, V

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Towards out-of-distribution generalizable predictions of chemical kinetics properties

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Handling Distribution Shifts on Graphs: An Invariance Perspective

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Graph information bottleneck

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Discovering Invariant Rationales for Graph Neural Networks

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Deconfounding to Explanation Evaluation in Graph Neural Networks

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A Recipe for Causal Graph Regression: Confounding Effects Revisited Unresolved cited work

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A Recipe for Causal Graph Regression: Confounding Effects Revisited mixup: Beyond Empirical Risk Minimization

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A Recipe for Causal Graph Regression: Confounding Effects Revisited RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks

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A Recipe for Causal Graph Regression: Confounding Effects Revisited write newline

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