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Nonlinear causal discovery with additive noise models
TLDR
It is shown that the basic linear framework can be generalized to nonlinear models and, in this extended framework, nonlinearities in the data-generating process are in fact a blessing rather than a curse, as they typically provide information on the underlying causal system and allow more aspects of the true data-Generating mechanisms to be identified.
Causal Effect Inference with Deep Latent-Variable Models
TLDR
This work builds on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect and shows its method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.
MAGMA: Generalized Gene-Set Analysis of GWAS Data
TLDR
The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn’s Disease while maintaining a correct type 1 error rate.
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
TLDR
Empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions.
Causal discovery with continuous additive noise models
TLDR
If the observational distribution follows a structural equation model with an additive noise structure, the directed acyclic graph becomes identifiable from the distribution under mild conditions, which constitutes an interesting alternative to traditional methods that assume faithfulness and identify only the Markov equivalence class of the graph, thus leaving some edges undirected.
Information-geometric approach to inferring causal directions
TLDR
This work defines independence via orthogonality in information space so that it can explicitly describe the kind of dependence that occurs between P"Y and P"X"|"Y making the causal hypothesis ''Y causes X'' implausible.
Inferring deterministic causal relations
TLDR
This paper considers two variables that are related to each other by an invertible function, and shows that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference.
On causal and anticausal learning
TLDR
The problem of function estimation in the case where an underlying causal model can be inferred is considered, and a hypothesis for when semi-supervised learning can help is formulated, and corroborate it with empirical results.
Sufficient Conditions for Convergence of the Sum–Product Algorithm
  • J. Mooij, H. Kappen
  • Mathematics, Computer Science
    IEEE Transactions on Information Theory
  • 8 April 2005
TLDR
Novel conditions are derived that guarantee convergence of the sum-product algorithm to a unique fixed point, irrespective of the initial messages, for parallel (synchronous) updates and this bound outperforms existing bounds.
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
TLDR
This work proposes an approach for solving causal domain adaptation problems that exploits causal inference and does not rely on prior knowledge of the causal graph, the type of interventions or the intervention targets, and demonstrates a possible implementation on simulated and real world data.
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