The Comparison of Causal Effect Estimation Methods at Missing at Random

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.