Wang, Arran ZeyuBorland, DavidGotz, DavidDiehl, AlexandraKucher, KostiantynMédoc, Nicolas2025-05-262025-05-262025978-3-03868-286-8https://doi.org/10.2312/evp.20251124https://diglib.eg.org/handle/10.2312/evp20251124Guidance methods are often employed in visual analytics systems to help users navigate complex datasets and discover meaningful insights. Guidance based on correlation is a common method that can steer users towards closely related variables. However, recent work has shown that guidance based on counterfactual subsets can more effectively capture and surface causal relationships. In this work we further explore these guidance methods by characterizing their performance by systematically introducing perturbations in both the data points generated from a ground truth causal graph, and the causal relationships in the graph itself. Our results indicate that while both guidance types exhibit similar sensitivity to global data point perturbations, counterfactual guidance can better capture perturbations affecting only a single dimension, and more effectively reflect changes in causal link strengths, indicating an improved ability to capture narrow data changes and causal relationships.Attribution 4.0 International License10.2312/evp.202511243 pages