Journal of Guangxi Normal University(Natural Science Edition) ›› 2023, Vol. 41 ›› Issue (1): 122-130.doi: 10.16088/j.issn.1001-6600.2022010502

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Estimating Interaction Effects Between a Treatment and Covariates Using the Rubin Causal Model

DU Jierui, CUI Xia*, LI Yuan   

  1. School of Economics and Statistics, Guangzhou University, Guangzhou Guangdong 510006, China
  • Received:2022-01-05 Revised:2022-02-28 Online:2023-01-25 Published:2023-03-07

Abstract: Based on the Rubin causal model, the interaction effects between the treatment and the covariates in the compliance sub-population are obtained, by estimating the complier average causal effect parameter with likelihood function. Simulation studies demonstrate that the proposed inference procedure performs well. The proposed method is applied to the data of the rural residents and the migrant residents in China in 2013, using Mincer earnings function to evaluate the impact of migrant work on the rate of return to education. The empirical results show thatthe rate of return education of dependent subgroups of migrant workers increases by 3.22%.

Key words: interaction effect, complier, average causal effect, likelihood function, migrant work, rate of return to education

CLC Number: 

  • O212
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