This work proposes a framework for computing CXs for inputs with missing values using flow-based generative models and Mixed-Integer Linear Programming (MILP) and demonstrates that this method is able to generate CXs with higher validity, and lower cost than state-of-the-art methods relying on imputation.
This approach is the first to efficiently generate CFs that are realistic, low-cost and feasible, and accommodates both hard feasible constraints, specified by domain knowledge users, and soft feasible constraints, inferred automatically via causal inference from the dataset.
Kleopatra Markou, V. Kalogeraki, Dimitrios Gunopulos· 0 citations
The Amortized Variational Counterfactual Generator (AVCG), a generalized optimization framework that formulates counterfactual generation as optimization over an arbitrary distribution of plausible predictive hypotheses rather than a single deterministic predictor, is proposed.
J. Duell, Alejandro Jimenez Rodriguez, Mahault Albarracin· 0 citations
Counterfactuals (CFs) are a widely studied approach for explaining machine learning models. They aim to identify a set of minimal and meaningful perturbations in input features required to alter a prediction of a model, thereby offering insights into decision boundaries. For instance, a CF may describe the changes need...
R. Wijethunga, Vinura Galwaduge, A. Sadhu et al.· IEEE Canadian Journal of Ele...· 0 citations
In the field of Explainable AI (XAI), counterfactual (CF) explanations interpret a model's decision by suggesting the changes to the input that would lead to a more favourable outcome. To be useful in practice, such an explanation should change few features and change them as little as possible, properties known as spa...
This work proposes counterfactual (CF) marginalisation as a test-time evaluation procedure for assessing robustness of classification models to such variables and demonstrates this framework's utility for quantitative robustness evaluation.
Yasin Ibrahim, Hermione Warr, R. Evans et al.· 0 citations
Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising concerns about their impact on demographic disparities. In this context, causal inference provides a principled basis for assessing fairness, bec...
Patrik Okanovic, T. Hoefler, Drago Plečko· 0 citations
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