Exploring data is crucial in data analysis, as it helps users understand and interpret the data
more effectively. However, performing effective data exploration requires in-depth …

EXplainable AI (XAI) is an essential topic to improve human understanding of deep neural
networks (DNNs) given their black-box internals. For computer vision tasks, mainstream …

With the incredible growth of the scale and complexity of datasets, creating proper
visualizations for users becomes more and more challenging in large datasets. Though …

There has been an increasing interest in enhancing the fairness of machine learning (ML).
Despite the growing number of fairness-improving methods, we lack a systematic …

Debugging performance anomalies in databases is challenging. Causal inference
techniques enable qualitative and quantitative root cause analysis of performance …

Data analysts often encounter two primary challenges while conducting exploratory data
analysis by SQL:(1) the need to skillfully craft SQL queries, and (2) the requirement to …

Understanding causal relations is vital in scientific discovery. The process of causal structure
learning involves identifying causal graphs from observational data to understand such …

Differentiable causal discovery has made significant advancements in the learning of
directed acyclic graphs. However, its application to real-world datasets remains restricted …

Causal discovery is a powerful technique for identifying causal relationships among
variables in data. It has been widely used in various applications in software engineering …

Techniques from the theory of causality have seen extensive use in natural and social
sciences, since they allow scientists to explicitly model assumptions and draw quantitative …