Interacting Treatments With Endogenous Takeup
研究了随机实验中两个处理存在内生不依从和交互作用时，工具变量估计量的因果解释，并提供了辅助条件和边界策略来识别因果参数，应用于评估辅导和金钱激励对大学生学业表现的影响。
ABSTRACT We study causal inference in randomized experiments (or quasi‐experiments) following a factorial design. There are two treatments, denoted and , and units are randomly assigned to one of four categories: treatment alone, treatment alone, joint treatment, or none. Allowing for endogenous non‐compliance with the two binary instruments representing the intended assignment, as well as unrestricted interference across the two treatments, we derive the causal interpretation of various instrumental variable estimands under more general compliance conditions than in the literature. In general, if treatment takeup is driven by both instruments for some units, it becomes difficult to separate treatment interaction from treatment effect heterogeneity. We provide auxiliary conditions and various bounding strategies that may help zero in on causally interesting parameters. We apply our results to a program randomly offering two different treatments to first‐year college students, namely, tutoring and financial incentives, in order to assess the effect of the treatments on academic performance.