CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images
This package addresses the problem of incorporating image data into causal inference for researchers in fields like earth observation, bio-medical, and social sciences, though it is incremental as it builds on existing methods by adapting them to new data types.
The paper introduces the causalimages R package, which enables causal inference with image and image sequence data, providing tools for tasks like decomposing treatment effect heterogeneity and controlling for confounding using images, thereby allowing researchers to integrate imagery into causal analyses in a fast and accessible manner.
The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imagery into the study of cause and effect. One set of functions enables image-based causal inference analyses. For example, one key function decomposes treatment effect heterogeneity by images using an interpretable Bayesian framework. This allows for determining which types of images or image sequences are most responsive to interventions. A second modeling function allows researchers to control for confounding using images. The package also allows investigators to produce embeddings that serve as vector summaries of the image or video content. Finally, infrastructural functions are also provided, such as tools for writing large-scale image and image sequence data as sequentialized byte strings for more rapid image analysis. causalimages therefore opens new capabilities for causal inference in R, letting researchers use informative imagery in substantive analyses in a fast and accessible manner.