Regulators and academics are increasingly interested in the causal effect that algorithmic actions of a digital platform have on consumption. We introduce a general causal inference problem we call the steerability of consumption that abstracts many settings of interest. Focusing on observational designs and exploiting the structure of the problem, we exhibit a set of assumptions for causal identifiability that significantly weaken the often unrealistic overlap assumptions of standard designs. The key novelty of our approach is to explicitly model the dynamics of consumption over time, viewing the platform as a controller acting on a dynamical system. From this dynamical systems perspective, we are able to show that exogenous variation in consumption and appropriately responsive algorithmic control actions are sufficient for identifying steerability of consumption. Our results illustrate the fruitful interplay of control theory and causal inference, which we illustrate with examples from econometrics, macroeconomics, and machine learning.

翻译：监管机构与学术界日益关注数字平台算法行为对消费产生的因果效应。我们提出一个称为"消费可引导性"的广义因果推断问题，该问题抽象了多个相关场景。通过聚焦观察性研究设计并利用问题结构特性，我们提出一组用于因果可识别性的假设，这些假设显著弱化了标准设计中往往不切实际的重叠假设。本方法的核心创新在于显式建模消费随时间变化的动态过程，将平台视为作用于动态系统的控制器。从该动态系统视角出发，我们证明：消费的外生波动与适当响应的算法控制行为相结合，足以识别消费的可引导性。研究结果展示了控制理论与因果推断之间的建设性互动，我们通过计量经济学、宏观经济学和机器学习领域的案例对此加以阐释。