Optimal Covariate Balancing Conditions in Propensity Score Estimation
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倾向得分估计中的最优协变量平衡条件
- 研究问题
- 倾向得分模型局部误设时，协变量平衡条件如何影响逆概率加权估计的偏差与效率，又应如何选择平衡函数？
- 方法与思路
- 作者分析协变量平衡倾向得分方法的局部误设性质，据此选择最优平衡函数，并以筛估计放宽参数模型限制。
- 材料陈述的贡献
- 摘要称所提估计量具有双重稳健性及局部半参数效率；筛估计扩展在较弱条件下具有全局效率和较小渐近偏差。
- 阅读关注点
- 应核对局部误设的定义、最优平衡函数与结果模型的关系，以及筛估计效率和偏差比较所需的具体条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要未说明识别条件、筛估计的具体假设及有限样本改善幅度。
原文摘要
Inverse probability of treatment weighting (IPTW) is a popular method for estimating the average treatment effect (ATE). However, empirical studies show that the IPTW estimators can be sensitive to the misspecification of the propensity score model. To address this problem, researchers have proposed to estimate propensity score by directly optimizing the balance of pre-treatment covariates. While these methods appear to empirically perform well, little is known about how the choice of balancing conditions affects their theoretical properties. To fill this gap, we first characterize the asymptotic bias and efficiency of the IPTW estimator based on the Covariate Balancing Propensity Score (CBPS) methodology under local model misspecification. Based on this analysis, we show how to optimally choose the covariate balancing functions and propose an optimal CBPS-based IPTW estimator. This estimator is doubly robust; it is consistent for the ATE if either the propensity score model or the outcome model is correct. In addition, the proposed estimator is locally semiparametric efficient when both models are correctly specified. To further relax the parametric assumptions, we extend our method by using a sieve estimation approach. We show that the resulting estimator is globally efficient under a set of much weaker assumptions and has a smaller asymptotic bias than the existing estimators. Finally, we evaluate the finite sample performance of the proposed estimators via simulation and empirical studies. An open-source software package is available for implementing the proposed methods.