VAINE: Visualization and AI for Natural Experiments
This tool addresses the problem of causal inference for researchers in fields like economics and healthcare, but it appears incremental as it builds on existing methods for natural experiments.
The paper tackles the challenge of identifying and understanding natural experiments from observational data by introducing VAINE, a visual analytics tool, and demonstrates its utility in validating causal relationships and estimating average treatment effects through usage scenarios.
Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson's paradox through two usage scenarios.