Artificial intelligence can spot when correlation means causation

A new artificial intelligence (AI) has allowed researchers at UCL and Babylon Health, for the first time, to demonstrate a useful and reliable way of sifting through masses of correlating data to spot when correlation means causation. By fusing old, overlapping and incomplete datasets this new method, inspired by quantum cryptography, paves the way for researchers to glean the results of medical trials that would otherwise be too expensive, difficult or unethical to run. The research is being published at the prestigious and peer-reviewed Association for Advancement of Artificial Intelligence (AAAI) conference in New York. Dr Saurabh Johri, Chief Science Officer at Babylon, said: "Until now, we have been limited to piecing together answers from studies that needed to capture all the data really neatly. But when we've seen a correlation between obesity and low vitamin D in one study, and obesity and heart failure in another, we have not been able to say whether vitamin D has a causal role in heart failure without doing another, hugely expensive clinical trial. Now we can put the pieces of the jigsaw together." Dr Ciarán Lee, Senior Research Scientist at Babylon and Honorary Senior Research Associate at UCL Physics & Astronomy, explained: "Scientists have it hammered into them that correlation does not mean causation; ice-cream sales don't cause sunburn despite rates of both shooting up during the summer. To find the exact cause of sunburn we whittle down or control as many variables as possible.