Open-source · 2023–

CausalImages

Jerzak & Daoud

An R package and workflow for causal inference when your key variables live in images — from satellite imagery to biomedical scans to social science measurement.

R package Remote sensing Computer vision Causal workflows
Concept

When images are the data

Many of the most important outcomes are hard (or expensive) to measure directly — but correlate with visual signals. CausalImages is built around a simple idea: make image-based measurement compatible with credible causal identification, transparent assumptions, and reproducible code.

Separate measurement from causality

Treat CV models as measurement devices, then do inference carefully.

Validate where it matters

Sensitivity checks, evaluation metrics, and domain-grounded diagnostics.

Reproducible pipelines

Focus on workflows that are easy to rerun, audit, and extend.
Workflow

Workflow and results

A representative pipeline: data, models, evaluation, and inference — with the goal of interpretable effect heterogeneity and robust policy insight.

Workflow diagram

Linked explainer: gci-overview

Effect heterogeneity