Causal inference is a statistical paradigm for quantifying causal effects using observational
data. It is a complex process, requiring multiple steps, iterations, and collaborations with …

Prompt-based interfaces for Large Language Models (LLMs) have made prototy** and
building AI-powered applications easier than ever before. However, identifying potential …

Machine learning models often learn latent embedding representations that capture the
domain semantics of their training data. These embedding representations are valuable for …

Big data and machine learning tools have jointly empowered humans in making data-driven
decisions. However, many of them capture empirical associations that might be spurious …

The anywidget project provides a specification and toolset for creating portable web-based
widgets for computational notebooks. It defines a standard for front-end widget code using …

Interactive visualization can support fluid exploration but is often limited to predetermined
tasks. Scripting can support a vast range of queries but may be more cumbersome for free …

Explainable AI (XAI) tools represent a turn to more human-centered and human-in-the-loop
AI approaches that emphasize user needs and perspectives in machine learning model …

The way of using computational notebooks is quite different between data science and
visual analytics. Data scientists focus on data exploration with the code, while visual …

Big data and machine learning tools have jointly empowered humans in making data-driven
decisions. Many of them capture empirical associations that might be spurious due to …