108 citations · 116 across the 6 of their papers we have counts for
11 papers · 1 filter
Causal normalizing flows: from theory to practice
Adrián Javaloy, Pablo Sánchez-Martín, Isabel Valera
In this work, we deepen on the use of normalizing flows for causal reasoning. Specifically, we first leverage recent results on non-linear ICA to show that causal models are identi…
Variational Mixture of HyperGenerators for Learning Distributions Over Functions
Batuhan Koyuncu, Pablo Sanchez-Martin, Ignacio Peis +2
Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces. However, they are computationally costly when dealing with infe…
A Ranking Approach to Fair Classification
Jakob Schoeffer, Niklas Kuehl, Isabel Valera
Algorithmic decision systems are increasingly used in areas such as hiring, school admission, or loan approval. Typically, these systems rely on labeled data for training a classif…
Scaling Guarantees for Nearest Counterfactual Explanations
Kiarash Mohammadi, Amir-Hossein Karimi, Gilles Barthe +1
Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e.g., loan approval or pretrial bail…
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf +1
Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addi…
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf +1
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships betwe…