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Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry

Serena Wang · Stephen Bates · P Aronow · Michael Jordan

MR1 & MR2 - Number 89
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Thu 2 May 8 a.m. PDT — 8:30 a.m. PDT
Oral presentation: Oral: Trustworthy ML
Sat 4 May 5 a.m. PDT — 6 a.m. PDT


From the social sciences to machine learning, it is well documented that metrics do not always align with social welfare. In healthcare, Dranove et al. (2003) showed that publishing surgery mortality metrics actually harmed sicker patients by increasing provider selection behavior. Using a principal-agent model, we analyze the incentive misalignments that arise from such average treated outcome metrics, and show that the incentives driving treatment decisions would align with maximizing total patient welfare if the metrics (i) accounted for counterfactual untreated outcomes and (ii) considered total welfare instead of averaging over treated patients. Operationalizing this, we show how counterfactual metrics can be modified to behave reasonably in patient-facing ranking systems. Extending to realistic settings when providers observe more about patients than the regulatory agencies do, we bound the decay in performance by the degree of information asymmetry between principal and agent. In doing so, our model connects principal-agent information asymmetry with unobserved heterogeneity in causal inference.

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