Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds

NeurIPS 2019 Nathan KallusAngela Zhou

Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to evaluate the fairness of such policies, we show that actually auditing their disparate impacts per standard observational metrics, such as true positive rates, is impossible since ground truths are unknown... (read more)

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