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"崩溃前沿"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和独立性违背的敏感性。我采用Masten & Poirier（2018）提出的$c$-依赖概念对独立性违背进行参数化，并允许违抗者比例大于零但小于依从者比例。我推导出LATE与平均处理效应（ATE）的识别集，其中边界是这两个敏感性参数的函数。利用这些边界，本文推导出LATE的崩溃前沿——即支持LATE相关结论成立所需的最弱假设组合。我们构建了崩溃前沿的一致样本类比估计量，并给出有效的自举推断程序。蒙特卡洛模拟表明该估计量具有优良的有限样本性质，实证应用显示Angrist & Evans（1998）关于家庭规模对失业影响结论对独立性和单调性的违背高度敏感。