Data scientists have embraced computational notebooks to author analysis code and
accompanying visualizations within a single document. Currently, although these media …

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 …

Machine Learning systems are, by now, an essential part of the software landscape. From
the development perspective this means a paradigmatic shift, which should be reflected in …

Computational notebooks like the Jupyter programming environment have been popular,
particularly for developing data-driven applications. One of its main benefits is that it easily …

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 …

Computational notebooks such as Jupyter are popular for exploratory data analysis and
insight finding. Despite the module-based structure, notebooks visually appear as a single …

Software development of modern, data-driven applications still relies on tools that use
interaction paradigms that have remained mostly unchanged for decades. While rich forms …

Software development of modern, data-driven applications still relies on tools that use
interaction paradigms that have remained mostly unchanged for decades. While rich forms …

Due to the long runtime of Data Science (DS) pipelines, even small programming mistakes
can be very costly, if they are not detected statically. However, even basic static type …

Editing documents written in a description language such as LaTeX is difficult. Few editors
improve the experience, and WYSIWYG interfaces for LaTeX are often limited. Yet, LaTeX is …