Causal Inference out of Control: Estimating the Steerability of Consumption

TL;DR

This paper introduces a novel causal inference framework for estimating how digital platforms can steer consumption, leveraging control theory and dynamical systems to relax traditional assumptions and improve identifiability.

Contribution

It presents a new approach modeling consumption as a dynamical system influenced by platform actions, enabling causal estimation with weaker assumptions.

Findings

Explicit modeling of consumption dynamics enhances causal identifiability.

Exogenous variation and responsive algorithms suffice for estimating steerability.

Applications demonstrated in econometrics, macroeconomics, and machine learning.

Abstract

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.