Optimal model averaged prediction of counter-factual outcomes for causal inference

2017 Fiscal Year Final Research Report

• Project/Area Number: 16K17102
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Economic statistics
• Research Institution: Kyoto Sangyo University
• Principal Investigator: YOSHIMURA Arihiro 京都産業大学, 経済学部, 講師 (40773982)
• Project Period (FY): 2016-04-01 – 2018-03-31
• Keywords: モデル選択 / モデル平均 / 政策評価法 / クロスセクション相関

Outline of Final Research Achievements

This study developed an optimal model averaged prediction method of individual counter-factual outcomes for causal inference. The main result of this study is to construct a model averaging method in the presence of cross-sectional dependence in the data and to derive the theoretical properties of the method. Moreover, the finite sample property of the method was investigated by large scale simulation experiments and the better performance of the method relative to the existing methods was found.

Free Research Field

計量経済学