Causal Priors and Their Influence on Judgements of Causality in Visualized Data.

"Correlation does not imply causation' is a famous mantra in statistical and visual analysis. However, consumers of visualizations often draw causal conclusions when only correlations between variables are shown. In this paper, we investigate factors that contribute to causal relationships users perceive in visualizations.

Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis.

Providing effective guidance for users has long been an important and challenging task for efficient exploratory visual analytics, especially when selecting variables for visualization in high-dimensional datasets. Correlation is the most widely applied metric for guidance in statistical and analytical tools, however a reliance on correlation may lead users towards false positives when interpreting causal relations in the data. In this work, inspired by prior insights on the benefits of counterfactual visualization in supporting visual causal inference, we propose a novel, simple, and efficient counterfactual guidance method to enhance causal inference performance in guided exploratory analytics based on insights and concerns gathered from expert interviews.

Using Counterfactuals to Improve Causal Inferences From Visualizations.

Traditional approaches to data visualization have often focused on comparing different subsets of data, and this is reflected in the many techniques developed and evaluated over the years for visual comparison. Similarly, common workflows for exploratory visualization are built upon the idea of users interactively applying various filter and grouping mechanisms in search of new insights. This paradigm has proven effective at helping users identify correlations between variables that can inform thinking and decision-making.

ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data.

Understanding cellular metabolism is important across biotechnology and biomedical research and has critical implications in a broad range of normal and pathological conditions. Here, we introduce the user-friendly web-based platform ImmCellFie, which allows the comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. We explain how to set up a run using publicly available omics data and how to visualize the results.

Computational Metrics Can Provide Quantitative Values to Characterize Arthroscopic Field of View.

Purpose: The purpose of this study was to determine the inter-rater reliability of arthroscopic video quality, determine correlation between surgeon rating and computational image metrics, and facilitate a quantitative methodology for assessing video quality.

Methods: Five orthopaedic surgeons reviewed 60 clips from deidentified arthroscopic shoulder videos and rated each on a four-point Likert scale from poor to excellent view. The videos were randomized, and the process was completed a total of three times.