Policy Learning with Confidence

TL;DR

This paper proposes a risk-aware policy selection rule that guarantees welfare thresholds with specified confidence, accounting for estimation uncertainty in social program allocations.

Contribution

It introduces a novel policy rule that explicitly incorporates estimation risk and provides welfare guarantees, advancing decision-making under uncertainty.

Findings

The rule maximizes estimated welfare for a given risk level.

Applied to social program funding with standard error estimates.

Ensures welfare exceeds thresholds with high confidence.

Abstract

This paper introduces a rule for policy selection in the presence of estimation uncertainty, explicitly accounting for estimation risk. The rule belongs to the class of risk-aware rules on the efficient decision frontier, characterized as policies offering maximal estimated welfare for a given level of estimation risk. Among this class, the proposed rule is chosen to provide a reporting guarantee, ensuring that the welfare delivered exceeds a threshold with a pre-specified confidence level. We apply this approach to the allocation of a limited budget among social programs using estimates of their marginal value of public funds and associated standard errors.