3 accepted papers
Machine learning (ML) models are increasingly used in high-stakes decisions, such as insurance pricing and pretrial detention, but often reproduce or amplify biases present in data. To mitigate discrimination, optimal transport (OT) offers a principled way to transform unfair model predictions into
Recently, optimal transport-based approaches have gained attention for deriving counterfactuals, e.g., to quantify algorithmic discrimination. However, in the general multivariate setting, these methods are often opaque and difficult to interpret. To address this, alternative methodologies have been
In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential tran…