报告题目:

Averaging Estimators of Heterogeneous Treatment Effect under Additive Models

报告人:

张新雨  研究员（中科院数学与系统科学研究院预测中心）

主持人:

王锦荣  教授（院长）

报告时间:

2022年11月9日星期三下午 15:00—16:00

腾讯会议:

#腾讯会议ID: 124 701 990

报告摘要: In the estimation heterogeneous treatment effect, model uncertainty often exists because of the uncertainty which variable should be used or other reasons. To handle the model uncertainty, we propose a novel model averaging method for estimating the heterogeneous treatment effect by assembling the estimations from multiple additive candidate models with certain weights. The weights are obtained by minimizing J-fold cross-validation, in which nearest neighbor matching is used to impute the unobserved potential outcome. We show that the proposed method is asymptotically optimal in the sense of achieving the lowest possible squared loss and can put the weight one to the correctly specified models. Both simulation study and empirical example show the superiority of our proposed estimator over other competitive methods.

报告人简介: 张新雨，中科院数学与系统科学研究院预测中心研究员。主要从事计量经济学和统计学的理论和应用研究工作，具体研究方向包括模型平均、机器学习和组合预测等，在统计学四大期刊和计量经济学顶级期刊JoE发表论文20余篇。担任SCI期刊《JSSC》领域主编、期刊《系统科学与数学》、《数理统计与管理》等的编委，是双法学会数据科学分会副理事长、国际统计学会当选会员，先后主持自科优秀和杰出青年基金项目。