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Model Average for Estimating Treatment Effects
澳门新葡8455最新网站:2017年05月04日 09:09 点击数:

报告人:张新雨

报告地点:澳门新葡8455最新网站四楼学术报告厅

报告澳门新葡8455最新网站:2017年05月06日星期六09:10-09:40

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报告摘要:

In many empirical investigations, particularly in fields such as economic policy, personalized medicine, and direct marketing, estimating a treatment effect on a response is a primary goal. Typically, we would like to select a statistical model based on sample data to estimate conditional treatment effects. However, the average from the candidate models often provides a more accurate estimate than the selection of a single candidate model. This paper proposes a new weight choice for model average estimate of the treatment effect conditional on covariates. We prove that our new model average estimator is asymptotically optimal in the sense of achieving the lowest possible squared error. In a simulation experiment we show that the proposed estimator compares favorably with those based on AIC and BIC weights. We apply the averaging method to evaluate the effect of the labor market program.

主讲人概况:

张新雨,中国科新葡京最新官网数学与系统科学研究院副研究员,获国家优秀青年科学基金支撑。

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