In this paper I develop a breakdown frontier approach to assess the sensitivity of Local Average Treatment Effects (LATE) estimates to violations of monotonicity and independence of the instrument. I parametrize violations of independence using the concept of $c$-dependence from Masten & Poirier (2018) and allow for the share of defiers to be greater than zero but smaller than the share of compliers. I derive identified sets for the LATE and the Average Treatment Effect (ATE) in which the bounds are functions of these two sensitivity parameters. Using these bounds, I derive the breakdown frontier for the LATE, which is the weakest set of assumptions such that a conclusion regarding the LATE holds. I derive consistent sample analogue estimators for the breakdown frontiers and provide a valid bootstrap procedure for inference. Monte Carlo simulations show the desirable finite-sample properties of the estimators and an empirical application shows that the conclusions regarding the effect of family size on unemployment from Angrist & Evans (1998) are highly sensitive to violations of independence and monotonicity.

翻译：本文提出一种突破边界方法，用于评估局部平均处理效应（LATE）估计值对工具变量单调性与独立性条件违反的敏感性。借鉴Masten & Poirier（2018）提出的$c$-依赖概念，本文对独立性偏离进行参数化建模，并允许违规者比例大于零但小于依从者比例。推导出LATE与平均处理效应（ATE）的识别集，其边界为两个敏感性参数的函数。基于这些边界，本文推导出LATE的突破边界——即保证关于LATE的结论成立的最弱假设组合。进一步构建突破边界的一致性样本模拟估计量，并给出有效的Bootstrap推断程序。蒙特卡洛模拟证明了估计量良好的有限样本性质，实证应用表明，Angrist & Evans（1998）关于家庭规模对失业影响的研究结论对独立性与单调性条件的违反高度敏感。