Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
This work addresses a critical issue for researchers and policymakers in fields like public policy and ecology, where decisions rely on non-tabular image data, but it is incremental as it builds on existing causal inference methods by adapting them to handle image confounding.
The paper tackles the problem of estimating causal effects when confounding factors are present in images, such as satellite imagery, by formalizing the challenge and proposing a methodology that uses machine learning to adjust for image confounding. It applies this approach to estimate the effects of policy interventions on poverty in African communities, demonstrating its utility in a real-world setting.
Observational studies of causal effects require adjustment for confounding factors. In the tabular setting, where these factors are well-defined, separate random variables, the effect of confounding is well understood. However, in public policy, ecology, and in medicine, decisions are often made in non-tabular settings, informed by patterns or objects detected in images (e.g., maps, satellite or tomography imagery). Using such imagery for causal inference presents an opportunity because objects in the image may be related to the treatment and outcome of interest. In these cases, we rely on the images to adjust for confounding but observed data do not directly label the existence of the important objects. Motivated by real-world applications, we formalize this challenge, how it can be handled, and what conditions are sufficient to identify and estimate causal effects. We analyze finite-sample performance using simulation experiments, estimating effects using a propensity adjustment algorithm that employs a machine learning model to estimate the image confounding. Our experiments also examine sensitivity to misspecification of the image pattern mechanism. Finally, we use our methodology to estimate the effects of policy interventions on poverty in African communities from satellite imagery.