2SLS with multiple treatments
研究了在存在多个处理变量且处理效应异质的情况下，两阶段最小二乘法（2SLS）识别的是什么，给出了2SLS识别出各处理正向加权平均效应的充要条件，并提供了可检验的含义。
We study what two-stage least squares (2SLS) identifies in models with multiple treatments under treatment effect heterogeneity. Two conditions are shown to be necessary and sufficient for the 2SLS to identify positively weighted sums of agent-specific effects of each treatment: average conditional monotonicity and no cross effects. Our identification analysis allows for any number of treatments, any number of continuous or discrete instruments, and the inclusion of covariates. We provide testable implications and present characterizations of choice behavior implied by our identification conditions.