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From the social sciences to machine learning, it has been well documentedthat metrics to be optimized are not always aligned with social welfare. Inhealthcare, Dranove et al. (2003) showed that publishing surgery mortalitymetrics actually harmed the welfare of sicker patients by increasing providerselection behavior. We analyze the incentive misalignments that arise from suchaverage treated outcome metrics, and show that the incentives driving treatmentdecisions would align with maximizing total patient welfare if the metrics (i)accounted for counterfactual untreated outcomes and (ii) considered totalwelfare instead of averaging over treated patients. Operationalizing this, weshow how counterfactual metrics can be modified to behave reasonably inpatient-facing ranking systems. Extending to realistic settings when providersobserve more about patients than the regulatory agencies do, we bound the decayin performance by the degree of information asymmetry between principal andagent. In doing so, our model connects principal-agent information asymmetrywith unobserved heterogeneity in causal inference.

Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry