Introduction Armed conflict severely impacts health, with indirect deaths often exceeding direct casualties two to four times, disproportionately affecting women and children. Although the magnitude ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

Health researchers need to fully understand the underlying assumptions to uncover cause and effect. Timothy Feeney and Paul Zivich explain Physicians ask, answer, and interpret myriad causal questions ...

Forbes contributors publish independent expert analyses and insights. I write about the economics of AI. When OpenAI’s ChatGPT first exploded onto the scene in late 2022, it sparked a global obsession ...

If the hyperscalers are masters of anything, it is driving scale up and driving costs down so that a new type of information technology can be cheap enough so it can be widely deployed. The ...

AI inference uses trained data to enable models to make deductions and decisions. Effective AI inference results in quicker and more accurate model responses. Evaluating AI inference focuses on speed, ...

Join us for a dynamic discussion celebrating the launch of Causal Inference and the People's Health, exploring the role of causal inference in advancing health equity and social justice. The symposium ...

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...

At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in ...