CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST 2014, 30(6) 607-619 DOI:      ISSN: 1001-4268 CN: 31-1256

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The Comparison of Causal Effect Estimation Methods at Missing at Random

Han Kaishan

School of Science, North University of China; School of Statsitics, Renmin University of China

Abstract��

When the dependent variable is missing at random, the paper first
proposes the four causal effect estimation methods: propensity scores weighted method (PW),
improved propensity score weighted method (IPW), augmented propensity weighted estimator
(AIPW), regression estimator (REG) and proves the unbiasedness and consistency of the four
methods. The paper also proves that AIPW method is double robustness. The four methods are
compared when the missing ratio is in different level. It is illuminated that AIPW is more
precise and more efficient than other methods. Finally, the causal effect of the American
academy of child and adolescent welfare survey data is estimated with the four methods and
the results are reached that children accept drug intervention service show no more serious
behavior problems than the children who don't accept drug abuse services.

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