Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry

TL;DR

This paper explores how counterfactual metrics can be designed to better align incentives with social welfare in healthcare and ranking systems, addressing issues of information asymmetry and causal inference.

Contribution

It introduces a framework for operationalizing counterfactual metrics that improve incentive alignment and analyzes the impact of information asymmetry on metric performance.

Findings

Counterfactual metrics align incentives with total welfare.

Performance degrades with increased information asymmetry.

Bounding performance loss links principal-agent asymmetry to causal heterogeneity.

Abstract

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.