CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images

TL;DR

CausalImages is an R package that facilitates causal inference using image and image sequence data across various fields, offering tools for effect decomposition, confounding control, and data embedding.

Contribution

It introduces novel functions for causal analysis with images, including effect heterogeneity decomposition and confounding adjustment, enhancing R's capabilities for image-based causal inference.

Findings

Enables decomposition of treatment effects by images using Bayesian methods

Provides tools for controlling confounding with image data

Supports fast image embedding and large-scale data handling

Abstract

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.