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部分干扰与增量倾向得分政策下的高效双重差分估计AI 解读

This paper develops efficient difference-in-differences (DID) estimation under partial interference with a cluster incremental propensity score (CIPS) policy. We define direct and spillover average treatment effects on the treated, establish their identification, and derive their efficient influence functions, from which we construct a cross-fitted estimator. Simulations evaluate its finite-sample performance. An application to China's New Rural Pension Scheme recovers the…

非随机缺失数据下的半参数双重差分：影子变量方法AI 解读

This paper considers a semiparametric difference-in-differences (DID) framework for identifying and estimating treatment effects on the treated (ATT) when outcomes are missing not at random (MNAR), and a fully observed shadow variable is available. The shadow variable is assumed to be associated with the outcome evolution but independent of the missingness process, conditional on covariates and the possibly unobserved outcome evolution. We establish the identification…

基于协变量平衡倾向得分的双重差分估计量AI 解读

This article develops a covariate balancing approach for the estimation of treatment effects on the treated (ATT) in a difference-in-differences (DID) research design when panel data are available. We show that the proposed covariate balancing propensity score (CBPS) DID estimator possesses several desirable properties: (i) local efficiency, (ii) double robustness in terms of consistency, (iii) double robustness in terms of inference, and (iv) faster convergence to the ATT…