  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.
