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Found 8 papers, 7 papers with code
Robust Estimation of Causal Heteroscedastic Noise Models
One solution to this problem is assuming that cause and effect are generated from a structural causal model, enabling identification of the causal direction after estimating the model in each direction.
Domain Generalisation via Risk Distribution Matching
Hence, we propose a compelling proposition: Minimising the divergences between risk distributions across training domains leads to robust invariance for DG.
Differentiable Bayesian Structure Learning with Acyclicity Assurance
Score-based approaches in the structure learning task are thriving because of their scalability.
Heteroscedastic Causal Structure Learning
The result is HOST (Heteroscedastic causal STructure learning), a simple yet effective causal structure learning algorithm that scales polynomially in both sample size and dimensionality.
Causal Inference via Style Transfer for Out-of-distribution Generalisation
A well-known existing causal inference method like back-door adjustment cannot be applied to remove spurious correlations as it requires the observation of confounders.
Diffeomorphic Information Neural Estimation
Mutual Information (MI) and Conditional Mutual Information (CMI) are multi-purpose tools from information theory that are able to naturally measure the statistical dependencies between random variables, thus they are usually of central interest in several statistical and machine learning tasks, such as conditional independence testing and representation learning.
Conditional Independence Testing via Latent Representation Learning
Detecting conditional independencies plays a key role in several statistical and machine learning tasks, especially in causal discovery algorithms.
Efficient Classification with Counterfactual Reasoning and Active Learning
Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision.
