116 citations · 173 across the 12 of their papers we have counts for
12 papers
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners
Elias Chaibub Neto
Linear residualization is a common practice for confounding adjustment in machine learning (ML) applications. Recently, causality-aware predictive modeling has been proposed as an…
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features
Elias Chaibub Neto, Phil Snyder, Solveig K Sieberts +1
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
Elias Chaibub Neto
Causal modeling has been recognized as a potential solution to many challenging problems in machine learning (ML). Here, we describe how a recently proposed counterfactual approach…
Towards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case
Elias Chaibub Neto
We propose a counterfactual approach to train ``causality-aware" predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., p…
Causality-based tests to detect the influence of confounders on mobile health diagnostic applications: a comparison with restricted permutations
Elias Chaibub Neto, Meghasyam Tummalacherla, Lara Mangravite +1
Machine learning practice is often impacted by confounders. Confounding can be particularly severe in remote digital health studies where the participants self-select to enter the…
Using permutations to assess confounding in machine learning applications for digital health
Elias Chaibub Neto, Abhishek Pratap, Thanneer M Perumal +4
Clinical machine learning applications are often plagued with confounders that can impact the generalizability and predictive performance of the learners. Confounding is especially…