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Nonlinear causal discovery with additive noise models
TLDR
We show that the linear–non-Gaussian causal discovery framework can be generalized to admit nonlinear functional dependencies as long as the noise on the variables remains additive. Expand
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Elements of Causal Inference: Foundations and Learning Algorithms
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Causal inference using invariant prediction: identification and confidence intervals
TLDR
We propose a new framework for causal inference in which we collect all models that do show invariance in their predictive accuracy across settings and interventions. Expand
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Kernel-based Conditional Independence Test and Application in Causal Discovery
TLDR
We propose a Kernel-based Conditional Independence test (KCI-test), by constructing an appropriate test statistic and deriving its asymptotic distribution under the null hypothesis of conditional independence. Expand
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Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
TLDR
We study how to distinguish X causing Y from Y causing X using only purely observational data, i.e., a finite i.i.d. sample drawn from the joint distribution PX,Y . Expand
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Counterfactual reasoning and learning systems: the example of computational advertising
TLDR
This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Expand
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Causal discovery with continuous additive noise models
TLDR
We show that 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. Expand
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CAM: Causal Additive Models, high-dimensional order search and penalized regression
TLDR
We develop estimation for potentially high-dimensional additive structural equation models that can be efficiently addressed using sparse regression techniques. Expand
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Identifiability of Gaussian structural equation models with equal error variances
We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structuralExpand
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On causal and anticausal learning
TLDR
We consider the problem of function estimation in the case where an underlying causal model can be inferred. Expand
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