This paper develops a finite population framework for analyzing causal effects in settings with imperfect compliance where multiple treatments affect the outcome of interest. Two prominent examples are factorial designs and panel experiments with imperfect compliance. I define finite population causal effects that capture the relative effectiveness of alternative treatment sequences. I provide nonparametric estimators for a rich class of factorial and dynamic causal effects and derive their finite population distributions as the sample size increases. Monte Carlo simulations illustrate the desirable properties of the estimators. Finally, I use the estimator for causal effects in factorial designs to revisit a famous voter mobilization experiment that analyzes the effects of voting encouragement through phone calls on turnout.

翻译：本文构建了一个有限总体框架，用于分析在非完全依从性情境下多种处理对目标结果产生影响的因果效应。两个典型示例是非完全依从性的因子设计与面板实验。本文定义了能够捕捉不同处理序列相对有效性的有限总体因果效应。针对一类丰富的因子效应与动态因果效应，本文提出了非参数估计量，并推导了其随样本量增加的有限总体分布。蒙特卡洛模拟展示了这些估计量的优良性质。最后，本文运用因子设计中的因果效应估计量，重新审视了一项著名的选民动员实验，该实验分析了通过电话进行投票鼓励对投票率的影响。