Transfer Estimates for Causal Effects across Heterogeneous Sites
异质地点间因果效应的迁移估计AI 解读 ↓
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异质地点间因果效应的迁移估计
- 研究问题
- 已在多个实验地点实施的干预，如何利用新目标地点的处理前结果及个体属性，将处理效应外推至具有不同人群特征的地点？
- 方法与思路
- 作者把基线视为函数型数据，非参数地选择有限维预测特征空间，并在给定有限实验地点和目标地点的设计基础框架中评价预测。
- 材料陈述的贡献
- 摘要称构造了最优预测基，并给出目标地点条件平均处理效应估计相对受约束最优预测器的收敛速度，以现金转移项目展示潜在收益。
- 阅读关注点
- 应核对跨地点迁移所需条件、基线函数如何编码未观测地点差异，以及受约束最优性的基准、特征维度选择和目标地点的验证方式。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出外推假设及增益数值，相对受约束预测器的保证不等于无条件正确迁移。
原文摘要
We consider the problem of extrapolating treatment effects across heterogeneous populations (``sites"/``contexts"). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several ``experimental" sites in which an intervention has already been implemented to a new ``target" site for which a baseline survey of unit-specific, pre-treatment outcomes and relevant attributes is available. Our approach treats the baseline as functional data, and this choice is motivated by the observation that unobserved site-specific confounders manifest themselves not only in average levels of outcomes, but also how these interact with observed unit-specific attributes. We consider the problem of determining the optimal finite-dimensional feature space in which to solve that prediction problem. Our approach is design-based in the sense that the performance of the predictor is evaluated given the specific, finite selection of experimental and target sites. Our approach is nonparametric, and our formal results concern the construction of an optimal basis of predictors as well as convergence rates for the estimated conditional average treatment effect relative to the constrained-optimal population predictor for the target site. We quantify the potential gains from adapting experimental estimates to a target location in an application to conditional cash transfer (CCT) programs using a combined data set from five multi-site randomized controlled trials.