An Alternative Doubly Robust Estimation in Causal Inference Model

Shaojie Wei , Gaorong Li , Zhongzhan Zhang

Communications in Mathematics and Statistics ›› 2024, Vol. 12 ›› Issue (4) : 659 -678.

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Communications in Mathematics and Statistics ›› 2024, Vol. 12 ›› Issue (4) : 659 -678. DOI: 10.1007/s40304-022-00308-4
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An Alternative Doubly Robust Estimation in Causal Inference Model

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Abstract

Doubly robust (DR) methods that employ both the propensity score and outcome models are widely used to estimate the causal effect of a treatment and generally outperform those methods only using the propensity score or the outcome model. However, without appropriately chosen the working models, DR estimators may substantially lose efficiency. In this paper, based on the augmented inverse probability weighting procedure, we derive a new estimating equation for the causal effect by the strategy of combining estimating equations. The resulting estimator by solving the new estimating equation retains doubly robust and can improve the efficiency under the misspecification of conditional mean working model. We further show the large sample properties of the proposed estimator under some regularity conditions. Through simulation experiments and a real data analysis, we illustrate that the proposed method is competitive with its competitors, which is in line with those implied by the asymptotic theory.

Keywords

Average treatment effect / Causal effect / Doubly robust method / Estimating equation / Inverse probability weighting / Semiparametric efficiency

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Shaojie Wei, Gaorong Li, Zhongzhan Zhang. An Alternative Doubly Robust Estimation in Causal Inference Model. Communications in Mathematics and Statistics, 2024, 12(4): 659-678 DOI:10.1007/s40304-022-00308-4

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Funding

National Natural Science Foundation of China(11771032)

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