论文标题
广义的李边界
Generalized Lee Bounds
论文作者
论文摘要
Lee（2009）是在存在选择偏见的情况下绑定平均因果效应的一种常见方法，假设对选择的治疗效应对所有受试者都具有相同的符号。本文概括了李的边界，以允许通过预处理协变量来识别这种效果的迹象，从而使标准（无条件）单调性放松到其条件类似物。在低维平滑且高维稀疏设计中提出了广义李边界的渐近理论。该论文还概括了李的边界以适应多个结果。在专注于JobCorps的职业培训计划时，我首先表明无条件的单调性不太可能保持，然后证明使用协变量来收紧界限。
Lee (2009) is a common approach to bound the average causal effect in the presence of selection bias, assuming the treatment effect on selection has the same sign for all subjects. This paper generalizes Lee bounds to allow the sign of this effect to be identified by pretreatment covariates, relaxing the standard (unconditional) monotonicity to its conditional analog. Asymptotic theory for generalized Lee bounds is proposed in low-dimensional smooth and high-dimensional sparse designs. The paper also generalizes Lee bounds to accommodate multiple outcomes. Focusing on JobCorps job training program, I first show that unconditional monotonicity is unlikely to hold, and then demonstrate the use of covariates to tighten the bounds.
