The climate policy debate has been dominated by economic estimates of the costs of policies to reduce greenhouse gas emissions. Yet the models used to derive those estimates are based on assumptions that have largely gone untested. The conventional approach embodies structural features that rule...

Researchers cannot definitively interpret what the framers of the United States Constitution had in mind when they wrote of the general Welfare. Nevertheless, welfare economics can contribute to policy choice in democracies. Specifying social welfare functions enables coherent analysis, by...

In medical treatment and elsewhere, it has become standard to base treatment intensity (dosage) on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to specify some dose of a new drug and compare it...

Economists have long studied policy choice by a social planner who aims to maximize welfare in democracies or other political systems where, in some sense, welfare is intended to express the well-being of a society rather than the personal preferences of a dictator. The motivation for studying...

A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y on the basis of observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly...

Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings...

The use of race measures in clinical prediction models and algorithms has become a highly contentious issue, driven by concerns that inclusion of race as a covariate exacerbates and perpetuates long-standing disparities in quality of health care provided to racial and ethnic minority patients....

We argue that comprehensive out-of-sample (OOS) evaluation using statistical decision theory (SDT) should replace the current practice of K-fold and Common Task Framework validation in machine learning (ML) research. SDT provides a formal framework for performing comprehensive OOS evaluation...