Lookahead counterfactual fairness requires that an individual's future status, not just the current decision, is equal in factual and counterfactual worlds; the paper gives a predictor that achieves this under linear causal models and gradient-based strategic responses.
Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
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abstract
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 transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets.
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2024 1verdicts
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Lookahead Counterfactual Fairness
Lookahead counterfactual fairness requires that an individual's future status, not just the current decision, is equal in factual and counterfactual worlds; the paper gives a predictor that achieves this under linear causal models and gradient-based strategic responses.