Post: Advancing medical treatment with causal machine learning

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 individualized.

Artificial intelligence is making progress in the medical arena. When it comes to imaging techniques and the calculation of health risks, there is a plethora of AI methods in development and testing phases. Wherever it is a matter of recognizing patterns in large data volumes, it is expected that machines will bring great benefit to humanity. Following the classical model, the AI compares information against learned examples, draws conclusions, and makes extrapolations.

Now an international team led by Professor Stefan Feuerriegel, Head of the Institute of Artificial Intelligence (AI) in Management at LMU, is exploring the potential of a comparatively new branch of AI for diagnostics and therapy. Can causal machine learning (ML) estimate treatment outcomes – and do so better than the ML methods generally used to date? Yes, says a landmark study by the group, which has been published in the prestigious journal Nature Medicine : causal ML can improve the effectiveness and safety of treatments.

In particular, the new machine learning variant offers "an abundance of opportunities for personalizing treatment strategies and thus individually improving the health of patients," write the researchers, who hail from Munich, Cambridge (United Kingdom), and Boston (United States) and include Stefan Bauer and Niki Kilbertus, professors of computer science at the Technical University of Munich (TUM) and group leaders at Helmholtz AI.

As regards machine […]

Advancing medical treatment with causal machine learning