On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry

Serena Wang, Stephen Bates, P Aronow, Michael Jordan
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1522-1530, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-wang24b, title = {On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry}, author = {Wang, Serena and Bates, Stephen and Aronow, P and Jordan, Michael}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {1522--1530}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/wang24b/wang24b.pdf}, url = {https://proceedings.mlr.press/v238/wang24b.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry %A Serena Wang %A Stephen Bates %A P Aronow %A Michael Jordan %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-wang24b %I PMLR %P 1522--1530 %U https://proceedings.mlr.press/v238/wang24b.html %V 238 %X 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.
APA
Wang, S., Bates, S., Aronow, P. & Jordan, M.. (2024). On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:1522-1530 Available from https://proceedings.mlr.press/v238/wang24b.html.

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