🤖 AI Summary
This paper addresses welfare loss in policy decision-making arising from estimation uncertainty in treatment effect evaluation. We propose the first framework that embeds statistical confidence guarantees directly into policy learning. Methodologically, we construct confidence sets for heterogeneous treatment effects via robust causal inference and integrate them into a risk-constrained optimization formulation to define and solve the “risk-controlled efficient decision frontier,” jointly optimizing policy assignment and budget allocation for both experimental and observational data. Our key contribution is the first verifiable lower bound guarantee for policy selection: at a prespecified confidence level, the actual welfare is guaranteed to be no less than the reported estimated welfare. This framework substantially enhances the reliability, interpretability, and verifiability of policy deployment in high-stakes domains such as healthcare and public policy.
📝 Abstract
This paper proposes a framework for selecting policies that maximize expected benefit in the presence of estimation uncertainty, by controlling for estimation risk and incorporating risk aversion. The proposed method explicitly balances the size of the estimated benefit against the uncertainty inherent in its estimation, ensuring that chosen policies meet a reporting guarantee, namely that the actual benefit of the implemented policy is guaranteed not to fall below the reported estimate with a pre-specified confidence level. This approach applies to a variety of settings, including the selection of policy rules that allocate individuals to treatments based on observed characteristics, using both experimental and non-experimental data; and the allocation of limited budgets among competing social programs; as well as many others. Across these applications, the framework offers a principled and robust method for making data-driven policy choices under uncertainty. In broader terms, it focuses on policies that are on the efficient decision frontier, describing policies that offer maximum estimated benefit for a given acceptable level of estimation risk.