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存在溢出效应时合成控制方法的识别与贝叶斯推断AI 解读

Summary The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption, ruling out spillover effects. However, interventions often affect not only treated but also untreated units. This study proposes a novel panel data method that extends the standard SCM to account for spillovers and estimate both treatment and spillover effects. The…

选择由谁选择：能源返利项目中的自选择定向政策AI 解读

We develop an optimal policy assignment rule that integrates two distinctive approaches commonly used in economics—targeting by observables and targeting through self‐selection . Our method can be used with experimental or quasi‐experimental data to identify who should be treated, be untreated, and self‐select to achieve a policymaker's objective. Applying this method to a randomized controlled trial on a residential energy rebate program, we find that targeting that…

政策约束下最优动态处理分配规则的估计AI 解读

Many policies involve dynamics in their treatment assignments where individuals receive sequential interventions over multiple stages. We study estimation of an optimal dynamic treatment regime that guides the optimal treatment assignment for each individual at each stage based on their history. We propose an empirical welfare maximization approach in this dynamic framework, which estimates the optimal dynamic treatment regime using data from an experimental or…

内生删失持续期模型的部分识别与推断AI 解读

Abstract This paper studies identification and inference in transformation models with endogenous censoring. Many kinds of duration models, such as the accelerated failure time model, proportional hazard model, and mixed proportional hazard model, can be viewed as transformation models. We allow the censoring of a duration outcome to be arbitrarily correlated with observed covariates and unobserved heterogeneity. We impose no parametric restrictions on either the…

处理效应异质性依赖未观测固定效应时的面板平均处理效应估计AI 解读

Summary This paper proposes a new panel data approach to identify and estimate the time‐varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the…