5 papers
Regret in Treatment Choice when Welfare Varies with an Uncertain Event: The Prediction-Threshold Problem
Jeff Dominitz, Charles F. Manski
We study maximum regret (MR) of binary treatment choice in a population with observed covariates x, when welfare varies with an uncertain binary event. We consider decision making…
A Decision Theoretic Perspective on Artificial Superintelligence: Coping with Missing Data Problems in Prediction and Treatment Choice
Jeff Dominitz, Charles F. Manski
Enormous attention and resources are being devoted to the quest for artificial general intelligence and, even more ambitiously, artificial superintelligence. We wonder about the im…
Partial Identification of Mean Achievement in ILSA Studies with Multi-Stage Stratified Sample Design and Student Non-Participation
Diego Cortes, Jeff Dominitz, Maximiliano Romero
International large-scale assessment (ILSA) studies collect information across education systems with the objective of learning about the population-wide distribution of student ac…
Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory
Jeff Dominitz, Charles F. Manski
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 validatio…
Using Total Margin of Error to Account for Non-Sampling Error in Election Polls: The Case of Nonresponse
Jeff Dominitz, Charles F. Manski
The potential impact of non-sampling errors on election polls is well known, but measurement has focused on the margin of sampling error. Survey statisticians have long recommended…