no code implementations • 9 Nov 2020 • Elias Chaibub Neto
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.
no code implementations • 9 Nov 2020 • Elias Chaibub Neto, Phil Snyder, Solveig K Sieberts, Larsson Omberg
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.
no code implementations • 20 Apr 2020 • Elias Chaibub Neto
Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML).
no code implementations • 12 Jan 2020 • Elias Chaibub Neto
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
no code implementations • 21 Feb 2018 • Elias Chaibub Neto
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).