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时变调节效应的选择后推断AI 解读

Causal effect moderation investigates how the effect of interventions (or treatments) on outcome variables changes based on observed characteristics of individuals, known as potential effect moderators. With advances in data collection, datasets containing many observed features as potential moderators have become increasingly common. High-dimensional analyses often lack interpretability, with important moderators masked by noise, while low-dimensional, marginal analyses…

通过高效数据聚合估计处理效应AI 解读

Data aggregation, also known as meta analysis, is widely used to combine knowledge on parameters shared in common (e.g., average treatment effect) between multiple studies. In this paper, we introduce an attractive data aggregation scheme that pools summary statistics from various existing studies. Our scheme informs the design of new validation studies and yields us unbiased estimators for the shared parameters. In our setup, each existing study applies a LASSO regression…