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There has been growing attention on how to effectively and objectively usecovariate information when the primary goal is to estimate the averagetreatment effect (ATE) in randomized clinical trials (RCTs). In this paper, wepropose an effective weighting approach to extract covariate information basedon the empirical likelihood (EL) method. The resulting two-sample empiricallikelihood weighted (ELW) estimator includes two classes of weights, which areobtained from a constrained empirical likelihood estimation procedure, wherethe covariate information is effectively incorporated into the form of generalestimating equations. Furthermore, this ELW approach separates the estimationof ATE from the analysis of the covariate-outcome relationship, which impliesthat our approach maintains objectivity. In theory, we show that the proposedELW estimator is semiparametric efficient. We extend our estimator to tacklethe scenarios where the outcomes are missing at random (MAR), and prove thedouble robustness and multiple robustness properties of our estimator.Furthermore, we derive the semiparametric efficiency bound of all regular andasymptotically linear semiparametric ATE estimators under MAR mechanism andprove that our proposed estimator attains this bound. We conduct simulations tomake comparisons with other existing estimators, which confirm the efficiencyand multiple robustness property of our proposed ELW estimator. An applicationto the AIDS Clinical Trials Group Protocol 175 (ACTG 175) data is conducted.

Empirical Likelihood Weighted Estimation of Average Treatment Effects