Causal Inference on Discrete Data Using Additive Noise Models

Peters, Jonas; Janzing, Dominik; Schölkopf, Bernhard

doi:10.1109/tpami.2011.71

Cited by 155 publications

(163 citation statements)

References 8 publications

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“…Both continuous and discrete ANMs satisfy the ICM and will be used for GWCS., The proposed method for genome-wide causation analysis and inferring multilevel causal genotypemethylation-expression-phenotype-disease network was applied to the ROSMAP dataset [34] with 432 individuals, 19…”

Section: Shared Genetic Loci Underlying Ad and T2dmmentioning

confidence: 99%

Shared Causal Paths underlying Alzheimer’s dementia and Type 2 Diabetes

Jiao

Wang

et al. 2020

Sci Rep

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Background: Although Alzheimer's disease (AD) is a central nervous system disease and type 2 diabetes mellitus (T2DM) is a metabolic disorder, an increasing number of genetic epidemiological studies show clear link between AD and T2DM. The current approach to uncovering the shared pathways between AD and T2DM involves association analysis; however, such analyses lack power to discover the mechanisms of the diseases. Methods:We develop novel statistical methods to shift the current paradigm of genetic analysis from association analysis to deep causal inference for uncovering the shared mechanisms between AD and T2DM, and develop pipelines to infer multilevel omics causal networks which lead to shifting the current paradigm of genetic analysis from genetic analysis alone to integrated causal genomic, epigenomic, transcriptional and phenotypic data analysis. To discover common causal paths from genetic variants to AD and T2DM, we also develop algorithms that can automatically search the causal paths from genetic variants to diseases and Results: The proposed methods and algorithms are applied to ROSMAP dataset with 432 individuals who simultaneously had genotype, RNA-seq, DNA methylation and some phenotypes. We construct multi-omics causal networks and identify 13 shared causal genes, 16 shared causal pathways between AD and T2DM, and 754 gene expression and 101 gene methylation nodes that were connected to both AD and T2DM in multi-omics causal networks. Conclusions:The results of application of the proposed pipelines for identifying causal paths to real data analysis of AD and T2DM provided strong evidence to support the link between AD and T2DM and unraveled causal mechanism to explain this link.

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Section: Shared Genetic Loci Underlying Ad and T2dmmentioning

confidence: 99%

Shared Causal Paths underlying Alzheimer’s dementia and Type 2 Diabetes

Jiao

Wang

et al. 2020

Sci Rep

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show abstract

“…However, a fundamental problem in this approach is the symmetry of the underlaying distribution; the joint distribution P (x, y) may be factorized as either P (x)P (y|x) or P (y)P (x|y). This means that we cannot infer the causal direction directly from the joint distribution, and additional assumptions must be made about the mechanisms that generate the data [17].…”

Section: A Causal Inferencementioning

confidence: 99%

Innovation Representation of Stochastic Processes With Application to Causal Inference

Painsky

Feder

2020

IEEE Trans. Inform. Theory

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Typically, real-world stochastic processes are not easy to analyze. In this work we study the representation of any stochastic process as a memoryless innovation process triggering a dynamic system. We show that such a representation is always feasible for innovation processes taking values over a continuous set. However, the problem becomes more challenging when the alphabet size of the innovation is finite. In this case, we introduce both lossless and lossy frameworks, and provide closed-form solutions and practical algorithmic methods. In addition, we discuss the properties and uniqueness of our suggested approach. Finally, we show that the innovation representation problem has many applications. We focus our attention to Entropic Causal Inference, which has recently demonstrated promising performance, compared to alternative methods.

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“…However, in a couple of papers by Peters et al [33,34], the authors extend the additive noise approach discussed in the previous section to the discrete case. While the variables take on discrete values, the causal relations follow the formal restrictions of the continuous case:…”

Section: Restrictions On Multinomial Distributionsmentioning

confidence: 99%