Assessing the effectiveness of treatments is crucial to ensure patient safety and personalize patient care. Recent innovations in ML offer new, data-driven methods to estimate treatment effects from ...

Real-world data (RWD) is increasingly used for causal inference in healthcare research, but generating credible, decision-ready insights requires more than access to data. It demands intentional ...

In a perspective published in Psychoradiology, researchers from Shanghai Jiao Tong University confronted causal inference in clinical neuroscience research and advocate for more clarity and ...

Mapping biological mechanisms in cellular systems is a fundamental step in early-stage drug discovery that serves to generate hypotheses on what disease-relevant molecular targets may effectively be ...

A significant shift is under way in artificial intelligence, and it has huge implications for technology companies big and small. For the past half-decade, most of the focus in AI has been on training ...

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.