Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry
This addresses a critical issue in policy and healthcare where metrics can harm vulnerable groups, offering a solution to improve decision-making and fairness.
The paper tackles the problem of incentive misalignment in performance metrics, such as healthcare outcomes, by proposing counterfactual metrics that account for untreated outcomes and total welfare to align provider incentives with patient welfare, and bounds performance decay due to information asymmetry.
From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare. In healthcare, Dranove et al. (2003) showed that publishing surgery mortality metrics actually harmed the welfare of sicker patients by increasing provider selection behavior. 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.