论文标题
因果推理的最佳恢复
Optimal Recovery for Causal Inference
论文作者
论文摘要
可以使用信号处理技术来解决因果推理的问题。例如，成功量化干预措施的因果关系以确定干预措施是否达到所需结果至关重要。我们提出了一种新的几何信号处理方法，用于经典的合成控制，称为椭圆形最佳恢复（EOPR），用于估计治疗单元的不可观察结果。 EOPR为政策评估者提供了最糟糕的案例和典型结果，以帮助决策。它是一种与主成分理论相关的近似理论技术，它恢复了未知的观测值，并且给定一个学到的信号类别和一组已知的观察结果。我们表明，相对于因果推断，相对于其他方法，EOPR可以改善治疗前的拟合和减轻治疗后估计的偏差。除了恢复感兴趣的单位之外，EOPR的优势是，它比产生的估计产生了最差的限制。我们在计量经济学文献中以及在COVID-19大流行的背景下评估了人工生成数据的方法
Problems in causal inference can be fruitfully addressed using signal processing techniques. As an example, it is crucial to successfully quantify the causal effects of an intervention to determine whether the intervention achieved desired outcomes. We present a new geometric signal processing approach to classical synthetic control called ellipsoidal optimal recovery (EOpR), for estimating the unobservable outcome of a treatment unit. EOpR provides policy evaluators with both worst-case and typical outcomes to help in decision making. It is an approximation-theoretic technique that relates to the theory of principal components, which recovers unknown observations given a learned signal class and a set of known observations. We show EOpR can improve pre-treatment fit and mitigate bias of the post-treatment estimate relative to other methods in causal inference. Beyond recovery of the unit of interest, an advantage of EOpR is that it produces worst-case limits over the estimates produced. We assess our approach on artificially-generated data, on datasets commonly used in the econometrics literature, and in the context of the COVID-19 pandemic, showing better performance than baseline techniques
