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允许工具变量直接效应的剖面 Anderson–Rubin 稳健检验AI 解读

Instrumental variable analyses often rely on the assumption that instruments affect the outcome only through the endogenous regressor. In many applications, researchers can defend only a plausible range for direct effects of instruments, while conventional sensitivity analyses may be unreliable when instruments are weak. This paper proposes the profiled Anderson--Rubin (pAR) test, which considers all direct effects within a prespecified range and retains a candidate effect…

保形化 Lee 推断：单调样本选择下个体治疗效应的预测集AI 解读

Treatment can change which outcomes are observed, so treated-selected and selected-control units need not represent the same latent population. This paper proposes conformalized Lee inference for counterfactual prediction under randomized treatment and monotone sample selection. The procedure uses treated-selected observations to fit and calibrate an arbitrary prediction rule and replaces the usual $(1-α)$ score quantile with the adjusted $(1-απ)$ quantile, where $π$ is the…