Academic Events

Prof.Han Hong 

Stanford University

Paper 1: Statistical Tests for Replacing Human Decision Makers with Algorithms

Abstract 1: This paper proposes a statistical framework of using artificial intelligence to improve human decision making. The performance of each human decision maker is benchmarked against that of machine predictions. We replace the diagnoses made by a subset of the decision makers with the recommendation from the machine learning algorithm. We apply both a heuristic frequentist approach and a Bayesian posterior loss function approach to abnormal birth detection using a nationwide data set of doctor diagnoses from prepregnancy checkups of reproductive-age couples and pregnancy outcomes. We find that our algorithm on a test data set results in a higher overall true positive rate and a lower false positive rate than the diagnoses made by doctors only.

Paper 2: Statistical Inference of Optimal Allocations I: Regularities and their Implications

Abstract 2: In this paper, we develop a functional differentiability approach for solving statistical optimal allocation problems. We derive Hadamard differentiability of the value functions through analyzing the properties of the sorting operator using tools from geometric measure theory. Building on our Hadamard differentiability results, we apply the functional delta method to obtain the asymptotic properties of the value function process for the binary constrained optimal allocation problem and the plug-in ROC curve estimator. Moreover, the convexity of the optimal allocation value functions facilitates demonstrating the degeneracy of first order derivatives with respect to the policy. We then present a double / debiased estimator for the value functions. Importantly, the conditions that validate Hadamard differentiability justify the margin assumption from the statistical classification literature for the fast convergence rate of plug-in methods.