演講者簡介 : Professor Kitagawa received his Ph.D. in Economics from Brown University at 2009. He is currently a Lecturer at University College London, London U.K. His research fields are Econometric Theory, Causal Inference, Bayesian Econometrics, Bayesian Robustness and Ambiguity, Model Selection and Averaging, Statistical Decision Theory, Statistical Learning Theory, and Applied Econometrics.
演講摘要 : The goal of many randomized experiments and quasi-experimental studies in economics is to inform policies that aim to raise incomes and reduce economic inequality. A utilitarian policy maximizing the sum of individual incomes may not be the best choice if it magnifies economic inequality and post-treatment redistribution of income is infeasible. This paper develops a method to estimate the optimal treatment assignment policy based on observable individual covariates when the policy objective is to maximize an equality-minded rank-dependent social welfare function, which puts higher weight on individuals with lower-ranked outcomes. We estimate the optimal policy by maximizing a sample analogue of the rank-dependent welfare over a properly constrained set of policies. Although an analytical characterization of the optimal policy under a rank-dependent social welfare is not available even with the knowledge of potential outcome distributions, we show that the average social welfare attained by our estimated policy converges to the maximal attainable welfare at n-1/2 rate uniformly over a large class of data distributions. We also show that this rate is minimax optimal. We provide an application of our method using the data from the National JTPA Study.
