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.

使用 causalimages R 包，研究人员能够利用图像和图像序列数据进行因果推断，并提供了将卫星和生物医学图像等新数据源整合到因果关系研究中的新工具。该软件包提供了一系列功能，包括基于图像的因果推断分析、通过可解释的贝叶斯框架分解图像的处理效应异质性、使用图像进行混淆控制、生成作为图像或视频内容的向量摘要的嵌入向量。此外，该软件包还提供了基础功能，如将大规模图像和图像序列数据写入序列化字节字符串以加快图像分析速度。因此，causalimages 在 R 中开启了用于因果推断的新功能，使研究人员能够以快速且易于操作的方式使用信息丰富的图像进行实质性分析。