Identifying the Cumulative Causal Effect of a Non-Binary Treatment from a Binary Instrument
研究了当处理强度可变时，如何用二元工具变量识别从最低到最高处理强度的累积因果效应，并给出了在标准假设不成立时的额外假设和边界估计方法。
Abstract The effect of a treatment may depend on the intensity with which it is administered. We study identification of ordered treatment effects with a binary instrument, focusing on the effect of moving from the treatment's minimum to maximum intensity. With arbitrary heterogeneity across units, standard IV assumptions (Angrist and Imbens, 1995) do not constrain this parameter, even among compliers. We consider a range of additional assumptions and show how they can deliver sharp, informative bounds. We illustrate our approach with two applications, involving the effect of (1) health insurance on emergency department usage, and (2) attendance in an after-school program on student learning.