The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

With the emergence of huge amounts of heterogeneous multi-modal data, including images, videos, texts/languages, audios, and multi-sensor data, deep learning-based methods have shown promising ...

Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, ...

Machines can learn not only to make predictions, but also to handle causal relationships. An international research team shows how this could make therapies safer, more efficient, and more ...

The manufacturing landscape is evolving rapidly, with intelligent systems increasingly promising to boost efficiency, quality, and overall competitiveness. Traditional machine learning (ML) has ...

The surge in enterprise AI has fueled interest in causal analysis. In this piece, I explore the threads that bind cause and effect - and how they can be applied across a range of industry scenarios.