A causal process model for algorithmic recourse introduces post-recourse stability conditions and copula-based methods to infer intervention effects from observational or paired data, with a distribution-free fallback when the model is rejected.
arXiv preprint arXiv:1912.03277 , year=
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Formalizing personalization as individual actionability in causal recourse shows hard constraints degrade validity and plausibility while revealing socio-demographic disparities in costs.
PACE is a modular neuro-symbolic system that uses Answer Set Programming to enforce domain constraints when generating actionable counterfactuals for an MLP classifier on the Adult Income dataset.
citing papers explorer
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Causal Algorithmic Recourse: Foundations and Methods
A causal process model for algorithmic recourse introduces post-recourse stability conditions and copula-based methods to infer intervention effects from observational or paired data, with a distribution-free fallback when the model is rejected.
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From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse
Formalizing personalization as individual actionability in causal recourse shows hard constraints degrade validity and plausibility while revealing socio-demographic disparities in costs.
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PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
PACE is a modular neuro-symbolic system that uses Answer Set Programming to enforce domain constraints when generating actionable counterfactuals for an MLP classifier on the Adult Income dataset.