Aleksey Tetenov

TL;DR: The authors proposed the empirical welfare maximization (EWM) method, which estimates a treatment assignment policy by maximizing the sample analog of average social welfare over a class of candidate treatment policies, and provided an asymptotically valid inference procedure for the population welfare gain obtained by exercising the EWM rule.

TL;DR: It is shown that when the propensity score is known, the average social welfare attained by EWM rules converges at least at n^(-1/2) rate to the maximum obtainable welfare uniformly over a minimally constrained class of data distributions, and this uniform convergence rate is minimax optimal.

TL;DR: In this paper, the problem of treatment choice between a status quo treatment with a known outcome distribution and an innovation whose outcomes are observed only in a representative finite sample was studied, and the authors derived exact finite sample solutions for experiments with normal, Bernoulli and bounded distributions of individual outcomes.

TL;DR: In this article, the problem of treatment choice between a status quo treatment with a known outcome distribution and an innovation whose outcomes are observed only in a finite sample is studied, where the regret is the expected welfare loss due to assigning inferior treatments.

TL;DR: In this paper, the authors consider a planner choosing treatments for observationally identical persons who vary in their response to treatment, and assume that the objective is to maximize a concave-monotone function f( ·) of the success rate and show that admissibility depends on the curvature of f ( ·).