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Found 5 papers, 0 papers with code
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
In this paper, we compare the linear residualization approach against the causality-aware confounding adjustment in anticausal prediction tasks, and show that the causality-aware approach tends to (asymptotically) outperform the residualization adjustment in terms of predictive performance in linear learners.
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features
In health related machine learning applications, the training data often corresponds to a non-representative sample from the target populations where the learners will be deployed.
Causality-aware counterfactual confounding adjustment for feature representations learned by deep models
Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML).
Towards causality-aware predictions in static machine learning tasks: the linear structural causal model case
While counterfactual thinking has been used in ML tasks that aim to predict the consequences of different actions, policies, and interventions, it has not yet been leveraged in more traditional/static supervised learning tasks, such as the prediction of discrete labels in classification tasks or continuous responses in regression problems.
Applications
Detecting Learning vs Memorization in Deep Neural Networks using Shared Structure Validation Sets
Here, we propose a novel permutation approach that can differentiate memorization from learning in deep neural networks (DNNs) trained as usual (i. e., using the real labels to guide the learning, rather than shuffled labels).
